Method, apparatus and device for multi-layer signal interactive propagation in multi-layer system

Abstract

The application provides a multi-layer signal interaction propagation method, device and equipment in a multi-layer system, and relates to the technical field of social network analysis and information propagation. The method comprises the following steps: acquiring basic information of a multi-layer complex system and information to be propagated; constructing a multi-layer hypergraph structure according to the basic information of the multi-layer complex system; determining an associated hyperedge set of each node in a public node set; performing an iterative screening operation from the public node set according to an initial coupling degree to obtain a seed node set; performing intra-layer propagation of the information to be propagated in a current hypergraph layer according to an initial propagation state and a preset intra-layer propagation rule; when it is detected that the information to be propagated is propagated in the current hypergraph layer, the information to be propagated is transferred to a next hypergraph layer for intra-layer propagation according to a preset cross-layer propagation rule until there is no newly added active node. The method of the application realizes efficient and accurate multi-layer influence propagation modeling and seed optimization, and improves the authenticity and timing accuracy of signal propagation simulation.

Description

Technical Field

[0001] This application relates to the field of social network analysis and information dissemination technology, and in particular to a method, apparatus and device for multi-layer signal interaction and propagation in a multi-layer system. Background Technology

[0002] With the widespread application of multi-layered complex systems in various fields, such as social-information coupled networks and power-communication infrastructure, the need for signal diffusion methods capable of accurately modeling high-order interactions and cross-layer propagation is becoming increasingly urgent. Such methods play a crucial role in conducting in-depth influence analysis and supporting intelligent decision-making.

[0003] In existing technologies, the influence of signal propagation is typically analyzed using Independent Cascade (IC) models, Hypergraph-based Independent Cascade (Hyper-IC) models, or influence maximization methods based on evolutionary computation. Specifically, the IC model simulates signal diffusion by having a node attempt to activate its neighboring nodes in a single step; the Hyper-IC model uses hyperedges to represent higher-order propagation at the group level; and influence maximization algorithms based on evolutionary computation are used for efficiently searching for seed nodes.

[0004] However, the above methods are poorly adaptable to multi-layered complex systems and suffer from large prediction bias and insufficient accuracy. Summary of the Invention

[0005] This application provides a method, apparatus, and device for multi-layer signal interaction propagation in a multi-layer system. By constructing a multi-layer hypergraph structure and introducing an initial coupling degree index and an adaptive update mechanism, combined with cross-layer synchronization rules and hierarchical propagation rules, it achieves efficient and accurate multi-layer influence propagation modeling and seed optimization, thereby improving the realism and timing accuracy of signal propagation simulation.

[0006] In a first aspect, this application provides a method for multi-layer signal interaction propagation in a multi-layer system, the method comprising:

[0007] To acquire basic information and information to be disseminated from a multi-layered complex system;

[0008] A multi-layered hypergraph structure is constructed based on the basic information of a multi-layered complex system. The multi-layered hypergraph structure contains multiple hyperlayers. Each hyperlayer shares a common set of nodes and its own set of hyperedges. The common set of nodes contains multiple nodes and the set of hyperedges contains multiple hyperedges.

[0009] Based on the common node set and the hyperedge set, determine the set of associated hyperedges for each node in the common node set;

[0010] Calculate the initial coupling degree of each node in the common node set based on the set of associated hyperedges of each node, and perform an iterative filtering operation from the common node set based on the initial coupling degree of each node to obtain the seed node set;

[0011] The initial propagation state of the multi-layer hypergraph structure is initialized based on the seed node set, and the information to be propagated is propagated within the current hypergraph layer according to the preset intra-layer propagation rules based on the initial propagation state.

[0012] Once the information to be propagated has been propagated in the current superlayer, the information will be moved to the next superlayer for intralayer propagation according to the preset cross-layer propagation rules, until no new active nodes are added.

[0013] In one possible implementation, the set of associated hyperedges for each node in the set of common nodes is determined based on the set of common nodes and the set of hyperedges, including:

[0014] Based on the common node set, determine the hyperedge to which each node belongs in each hyperlayer as the associated hyperedge set of each node.

[0015] In one possible implementation, the initial coupling degree of each node in the common node set is calculated based on the set of associated hyperedges of each node, including:

[0016] Based on the set of associated hyperedges of each node, determine the set of neighboring nodes that each node in the common node set is associated with through hyperedges in the corresponding hyperlayer.

[0017] Based on the common node set and the set of neighbor nodes that each node is associated with through hyperedges in the corresponding superlayer, determine the total number of non-repeating neighbor nodes that each node in the common node set is associated with in the corresponding superlayer, and use the total number of non-repeating neighbor nodes as the initial coupling degree of the corresponding node.

[0018] In one possible implementation, the initial coupling degree is calculated using the following formula:

[0019]

[0020] In the formula, C(v) represents the initial coupling degree of node v. Let represent the set of neighboring nodes that node v is connected to via hyperedges at level l, where is the set of neighboring nodes that node v is connected to via hyperedges at level l. , This represents the union of the sets of the L neighboring nodes of node v in the superlayer.

[0021] In one possible implementation, a seed node set is obtained by iteratively filtering the common node set based on the initial coupling degree of each node, including:

[0022] The nodes in the common node set are sorted according to their initial coupling degree, and the node with the highest initial coupling degree is selected as the seed node.

[0023] Based on the seed node, determine whether other nodes in the common node set have no common hyperedge with the seed node in any hyperlayer. If so, keep the initial coupling degree of the node unchanged; otherwise, update the state of all neighboring nodes of the seed node.

[0024] Based on the updated status of each node, determine the number of seed nodes associated with each node;

[0025] Based on the initial coupling degree of each node and the number of associated seed nodes, determine the updated adaptive coupling degree of each node, and then jump to the step of sorting the nodes in the common node set according to the initial coupling degree of each node until a specified number of seed nodes are selected.

[0026] All selected seed nodes are combined into a seed node set.

[0027] In one possible implementation, the formula for determining the updated adaptive coupling degree of each node, based on the initial coupling degree of each node and the number of associated seed nodes, is as follows:

[0028]

[0029] In the formula, This represents the adaptive coupling degree of node v after the update. S(v) represents the adaptive coupling degree or initial coupling degree of node v before the update, and S(v) represents the number of associated seed nodes.

[0030] In one possible implementation, initializing the propagation state of the multi-layer hypergraph structure based on the seed node set includes:

[0031] Set the initial state of all hyperedges in the multi-layer hypergraph structure to the inactive state;

[0032] All nodes in the seed node set of the multi-level hypergraph structure are set to active state, and all nodes in the common node set but not in the seed node set of the multi-level hypergraph structure are set to inactive state.

[0033] Secondly, this application provides a multi-layer signal interaction propagation device for a multi-layer system, the device comprising:

[0034] The acquisition module is used to acquire basic information and information to be disseminated from a multi-layered complex system.

[0035] The hypergraph structure construction module is used to construct a multi-layer hypergraph structure based on the basic information of a multi-layer complex system. The multi-layer hypergraph structure contains multiple hyperlayers, and each hyperlayer shares a common set of nodes and its own set of hyperedges. The common set of nodes contains multiple nodes, and the set of hyperedges contains multiple hyperedges.

[0036] The association module is used to determine the associated hyperedge set of each node in the common node set based on the common node set and the hyperedge set.

[0037] The calculation module is used to calculate the initial coupling degree of each node in the common node set based on the set of associated hyperedges of each node, and to perform an iterative filtering operation from the common node set based on the initial coupling degree of each node to obtain the seed node set.

[0038] The interactive propagation module is used to initialize the initial propagation state of the multi-layer hypergraph structure based on the seed node set, and to propagate the information to be propagated within the current hypergraph layer according to the preset intra-layer propagation rules based on the initial propagation state.

[0039] The interactive propagation module is also used to, when it is detected that the information to be propagated has been propagated in the current superlayer, jump to the next superlayer according to the preset cross-layer propagation rules for intra-layer propagation, until no new active nodes are added.

[0040] In one possible implementation, the association module is further configured to determine, based on the common node set, the hyperedge to which each node belongs in each hyperlayer as the associated hyperedge set of each node.

[0041] In one possible implementation, the calculation module is further configured to determine, based on the set of associated hyperedges of each node, the set of neighboring nodes that each node in the common node set is associated with through hyperedges in the corresponding hyperlayer.

[0042] The calculation module is also used to determine the total number of non-repeating neighbor nodes associated with each node in the common node set in the corresponding superlayer based on the common node set and the set of neighbor nodes associated with each node in the corresponding superlayer through hyperedges, so as to use the total number of non-repeating neighbor nodes as the initial coupling degree of the corresponding node.

[0043] In one possible implementation, the formula for calculating the initial coupling degree in the calculation module is:

[0044]

[0045] In the formula, C(v) represents the initial coupling degree of node v. Let represent the set of neighboring nodes that node v is connected to via hyperedges at level l, where is the set of neighboring nodes that node v is connected to via hyperedges at level l. , This represents the union of the sets of the L neighboring nodes of node v in the superlayer.

[0046] In one possible implementation, the calculation module is further configured to sort the nodes in the common node set according to the initial coupling degree of each node, and obtain the node with the largest initial coupling degree as the seed node.

[0047] The calculation module is also used to determine, based on the seed node, whether other nodes in the common node set have no common hyperedge with the seed node in each hyperlayer. If so, the initial coupling degree of the node remains unchanged; otherwise, the state of all neighboring nodes of the seed node is updated.

[0048] The calculation module is also used to determine the number of seed nodes associated with each node based on the updated status of each node;

[0049] The calculation module is also used to determine the updated adaptive coupling degree of each node based on the initial coupling degree of each node and the number of associated seed nodes, and jump to the step of sorting the nodes in the common node set according to the initial coupling degree of each node until a specified number of seed nodes are selected.

[0050] The calculation module is also used to form a seed node set from all selected seed nodes.

[0051] In one possible implementation, the calculation module determines the updated adaptive coupling degree of each node based on its initial coupling degree and the number of associated seed nodes using the following formula:

[0052]

[0053] In the formula, This represents the adaptive coupling degree of node v after the update. S(v) represents the adaptive coupling degree or initial coupling degree of node v before the update, and S(v) represents the number of associated seed nodes.

[0054] In one possible implementation, the interactive propagation module is also used to set the initial state of all hyperedges in the multi-layer hypergraph structure to an inactive state.

[0055] The interactive propagation module is also used to set all nodes in the seed node set of the multi-layer hypergraph structure to an active state, and to set all nodes in the common node set but not in the seed node set of the multi-layer hypergraph structure to an inactive state.

[0056] Thirdly, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor.

[0057] The memory stores the instructions that the computer executes.

[0058] The processor executes computer execution instructions stored in memory to implement a multi-layer signal interaction propagation method for a multi-layer system, as described in the first aspect of the invention.

[0059] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement a multi-layer signal interaction propagation method for a multi-layer system as described in the first aspect of the invention.

[0060] Fifthly, this application provides a computer program product, including a computer program, which, when executed by a processor, is used to implement a multi-layer signal interaction propagation method for a multi-layer system as described in the first aspect of the invention.

[0061] Based on the implementation methods provided in the above aspects, this application can be further combined to provide more implementation methods.

[0062] This application provides a method, apparatus, and device for multi-layer signal interaction propagation in a multi-layer system, comprising: first, acquiring basic information of the multi-layer complex system and information to be propagated; then, constructing a multi-layer hypergraph structure based on the basic information of the multi-layer complex system, wherein the multi-layer hypergraph structure contains multiple hyperlayers, each hyperlayer sharing a common node set and its own hyperedge set, the common node set containing multiple nodes, and the hyperedge set containing multiple hyperedges; subsequently, determining the associated hyperedge set of each node in the common node set based on the common node set and the hyperedge set; next, calculating the initial coupling degree of each node in the common node set based on the associated hyperedge set of each node, and performing an iterative filtering operation from the common node set based on the initial coupling degree of each node to obtain a seed node set; then, initializing the initial propagation state of the multi-layer hypergraph structure based on the seed node set, and propagating the information to be propagated within the current hyperlayer according to the initial propagation state and a preset intra-layer propagation rule; finally, when it is detected that the information to be propagated has been propagated in the current hyperlayer, the information to be propagated is moved to the next hyperlayer according to a preset cross-layer propagation rule for intra-layer propagation, until no new active nodes are added. The following technical effects were achieved: By acquiring basic information and information to be propagated from a multi-layered complex system, a multi-layered hypergraph structure was constructed, the set of associated hyperedges of nodes was determined, the initial coupling degree was calculated, and a seed node set was selected. Then, intra-layer and cross-layer propagation were performed, realizing the efficient and accurate propagation of information to be propagated in the multi-layered system. It fully considers the complex structure of the multi-layered system and the complex relationships between nodes, possessing strong adaptability and practicality, and providing an effective solution to the information propagation problem in multi-layered systems. By constructing a multi-layered hypergraph structure and integrating high-order interaction modeling and cross-layered collaborative propagation mechanisms, the accuracy of characterizing signal diffusion behavior and the temporal controllability in complex systems are significantly improved. It can be widely applied to scenarios such as fault cascading prediction, critical node identification, information propagation simulation, and infrastructure resilience analysis. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0065] Figure 1 A flowchart illustrating a multi-layer signal interaction propagation method in a multi-layer system, provided as an embodiment of this application;

[0066] Figure 2 A schematic diagram of a multi-layer signal interaction propagation device for a multi-layer system is provided as an embodiment of this application;

[0067] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0068] Figure label:

[0069] 210 - Acquisition Module; 220 - Hypergraph Structure Construction Module; 230 - Association Module; 240 - Computation Module; 250 - Interactive Propagation Module;

[0070] 310 - Processor; 320 - Memory; 330 - Communication components; 340 - Bus. Detailed Implementation

[0071] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0072] In the embodiments of this application, the terms "first" and "second" are used to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that "first" and "second" do not necessarily imply difference. It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate that something is being used as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in this application should not be construed as being better or more advantageous than other embodiments or design schemes. Specifically, the use of "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner. In the embodiments of this application, "at least one" refers to one or more, and "more than one" refers to two or more.

[0073] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in one or more embodiments of this specification are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of related data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0074] It should be noted that the phrase "at...time" in the embodiments of this application can refer to the instant at which a certain situation occurs, or to a period of time after the occurrence of a certain situation; the embodiments of this application do not specifically limit this. Furthermore, the multi-layer signal interaction propagation method for a multi-layer system provided in the embodiments of this application is merely an example; a multi-layer signal interaction propagation method for a multi-layer system may also include more or less content.

[0075] With the widespread application of multi-layered complex systems (such as social-information coupled networks, power-communication interconnected systems, and multi-layered architectures of intelligent transportation), close cross-layer dependencies and high-order group interactions exist between different layers within the system. In such systems, the propagation of signals (such as fault alarms, control commands, and promotional information) is often not limited to a single layer but also spreads across multiple functional layers through shared nodes. Therefore, there is an urgent need for a signal diffusion modeling method that can simultaneously characterize high-order group interactions and cross-layer collaborative propagation mechanisms to support accurate impact analysis, timely early warning, and intelligent decision-making.

[0076] In existing technologies, the analysis of the influence of signal propagation mainly employs the following methods.

[0077] The IC model, based on the assumption that a node, once activated for the first time, will attempt to activate each of its neighboring nodes with a fixed probability, simulates the diffusion of information in a network. It is one of the classic foundational models for studying information diffusion and influence propagation in social networks. However, it is only applicable to binary relation networks and cannot represent the collaborative effects of multiple nodes.

[0078] The Hyper-IC model, to overcome the limitation of ordinary graphs that can only describe binary relationships, introduces a hypergraph structure on top of the IC model. Multiple nodes are connected simultaneously through hyperedges, thus depicting high-order interaction propagation at the group level. The propagation process is extended to a bidirectional propagation mechanism of "nodes activating hyperedges—hyperedges activating nodes." Although connecting multiple nodes through hyperedges supports group-level propagation modeling, it is still limited to a single-layer structure and lacks description of cross-layer state interactions.

[0079] In addition, there are influence maximization methods based on evolutionary computation. These methods introduce swarm intelligence algorithms (such as genetic algorithms, particle swarm optimization, and differential evolution) into the influence maximization problem to quickly search for the optimal set of seed nodes in large-scale networks, thereby reducing the computational complexity of propagation simulations. Although this can accelerate the search process for seed nodes, its analysis process relies on a large number of propagation simulations and does not consider the multi-layer coupling characteristics, resulting in large prediction bias, unstable convergence, and low computational efficiency in multi-layer systems.

[0080] Therefore, due to the complex interlayer coupling relationships and high-order group interaction characteristics of multi-layer systems, existing methods are poorly adaptable to complex multi-layer systems, struggling to accurately characterize the propagation mechanisms. This results in significant deviations in predicting the influence of signal propagation, and the accuracy fails to meet practical requirements. Therefore, there is an urgent need for a technical solution that can effectively address these issues to improve the realism and timing accuracy of signal propagation simulation in complex multi-layer systems.

[0081] Based on this, this application proposes a method, apparatus, and device for multi-layer signal interaction propagation in multi-layer systems, applicable to the fields of social network analysis and information dissemination, aiming to solve the aforementioned technical problems of the prior art. By constructing a multi-layer hypergraph structure containing multiple hyperlayers, each layer shares the same common set of nodes, and each hyperlayer consists of a set of nodes and a set of hyperedges; based on the hyperedge sets associated with each node, the initial coupling degree of each node is calculated, and a set of high-influence seed nodes is iteratively selected accordingly; a cross-layer synchronization rule is introduced, meaning that once any node is first activated in any layer, it is immediately activated synchronously in all layers; and a layer-order propagation rule is adopted, meaning that intra-layer propagation is performed cyclically between each hyperlayer in a preset order until no new activated nodes are added; simultaneously, an adaptive update mechanism can be combined to dynamically decay the node propagation potential based on the number of selected seeds in the neighborhood, suppressing the overlap of cross-layer and intra-layer influences. Through the above technical means, a unified modeling of high-order interactions and cross-layer propagation in multi-layer systems is achieved, significantly improving the realism and temporal accuracy of propagation simulation while effectively reducing the computational complexity of the influence maximization problem.

[0082] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0083] Figure 1 This is a flowchart illustrating a method for multi-layer signal interaction propagation in a multi-layer system, provided as an embodiment of this application. Figure 1 As shown, the method includes:

[0084] S101. Obtain basic information and information to be disseminated from a multi-layered complex system.

[0085] In this embodiment, the executing entity of a multi-layer signal interaction propagation method in a multi-layer system can be a data processing server or other devices with data processing capabilities, such as laptops, personal computers, tablets, etc. The data processing server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server, etc., without specific limitations. For ease of description, this embodiment uniformly describes the executing entity of a multi-layer signal interaction propagation method in a multi-layer system as a server. Specifically, the basic information of a multi-layer complex system can cover all levels and elements of the system, including the system topology, the constituent units of each functional layer, shared entities between layers, interaction relationships between nodes, and historical state data, etc.

[0086] For example, in a social-information coupled network, basic information may include a user's social relationships (such as friend lists, following relationships, etc.), the user's basic attributes (such as age, hobbies, etc.), the type of information (such as news, entertainment, technology, etc.), and the time when the information was published.

[0087] In power-communication infrastructure, basic information can include the connection relationships of power equipment, the topology of communication networks, the operating status of equipment, power load conditions, communication data traffic, and cross-domain nodes such as substations, transmission lines, communication base stations, and control centers.

[0088] The information to be disseminated can be a signal that needs to be spread throughout the system. It can take various forms of data, such as text, images, and audio, or it can be specific instructions, signals, or knowledge. Specifically, it can be fault alarms, control commands, content with a specific theme, important notices and announcements, influential opinions and viewpoints, or promotional information, used to trigger subsequent dissemination processes. For example, in a social network, the information to be disseminated could be a message about promoting a new product; in a power system, it could be a notice about a power outage for maintenance. This information can be obtained through data interfaces extracted from relevant databases or collected manually.

[0089] S102. Construct a multi-layered hypergraph structure based on the basic information of a multi-layered complex system.

[0090] In this embodiment, the multi-layer hypergraph structure includes multiple hyperlayers, each hyperlayer sharing a common node set and its own hyperedge set. The common node set contains multiple nodes, and the hyperedge set contains multiple hyperedges.

[0091] Specifically, based on the aforementioned fundamental information of a multi-layered complex system, a multi-layered hypergraph structure can be constructed. This structure can consist of multiple hyperlayers, each corresponding to a specific functional layer or logical view of the system. All hyperlayers share the same common set of nodes, which contains all entity nodes in the system. Each hyperlayer also contains an independent set of hyperedges, where each hyperedge can connect two or more nodes, used to characterize high-order interactions involving multiple entities within that layer. By introducing hyperedges, the limitation of traditional graph models, which can only express binary relationships, is effectively overcome.

[0092] In other words, a multi-layered hypergraph structure consists of multiple hyperlayers, each of which can be used to describe a specific level or aspect of the multi-layered system. All hyperlayers share a common set of nodes, enabling state linkage and information interaction between different layers through the same entity via a shared node mechanism. This common set of nodes contains all the nodes involved in the multi-layered system. For example, in a multi-layered hypergraph structure of a social network, the common set of nodes could include all user nodes. In power-communication infrastructure, the common set of nodes could include all power equipment and communication nodes.

[0093] Each superlayer has its own set of hyperedges, which are edges connecting multiple nodes and can represent complex relationships between them. Unlike traditional graph structures where edges can only connect two nodes, hyperedges can connect any number of nodes. They are a special type of edge that can connect multiple nodes and can be used to represent higher-order propagation relationships at the group level, thus providing a more flexible description of many-to-many relationships between nodes in a multi-layered system. For example, in a social network, a hyperedge can connect a group of friends participating in the same activity, or a group of users interested in the same topic, indicating group-level communication and propagation among these users. In a power system, a hyperedge can connect multiple interconnected power devices, representing complex energy transfer and collaborative relationships among these devices.

[0094] Based on the collected basic information of the multi-layered complex system, the entities in the system can be abstracted into nodes and added to a common node set.

[0095] For each hyperlayer, the complex relationships between nodes are determined based on the characteristics of that layer or aspect, and these relationships are represented using hyperedges. For example, in the online social hyperlayer of a social network, hyperedges can be constructed based on users' following relationships; in the offline social hyperlayer, hyperedges can be constructed based on the shared activities that users participate in.

[0096] The constructed hyperedges are added to the hyperedge set of the corresponding hyperlayer, thus completing the construction of the multi-layer hypergraph structure.

[0097] S103. Based on the common node set and the superedge set, determine the associated superedge set of each node in the common node set.

[0098] Specifically, for each node in the common node set, all superlayers are traversed, and all hyperedges in which the node participates in each superlayer are collected to form the set of associated hyperedges for that node. The set of associated hyperedges can comprehensively reflect the interaction breadth, group collaboration strength, and cross-layer activity of the corresponding node in the entire multi-layer system, and is a key basis for subsequent coupling quantification and seed selection.

[0099] In other words, for each node in the common node set, the hyperedge set of all hyperlayers is traversed. During the traversal, each hyperedge is checked to see if it contains the current node. If a hyperedge contains the current node, it is added to the current node's associated hyperedge set. In this way, all the complex relationships that each node in the common node set participates in across different hyperlayers can be found, i.e., the associated hyperedge set. For example, in a social network, for a specific user node, its associated hyperedge set can include not only the follow relationship hyperedges from different online social platforms' social hyperlayers, but also the shared activity hyperedges from offline social hyperlayers.

[0100] S104. Calculate the initial coupling degree of each node in the common node set based on the set of associated hyperedges of each node, and perform iterative filtering operation from the common node set based on the initial coupling degree of each node to obtain the seed node set.

[0101] Specifically, based on the set of associated hyperedges for each node, the initial coupling degree of each node can be calculated. The initial coupling degree can be determined based on the following factors, for example:

[0102] The total number of hyperedges in which nodes participate (reflecting interaction frequency); the number of nodes covered by each hyperedge (reflecting the scale of group influence); the number of layers in which nodes are distributed across different hyperlayers (reflecting cross-layer pivotality); and the historical activation frequency or weight of hyperedges (used to introduce dynamic priors).

[0103] Subsequently, a preset filtering strategy can be used, such as selecting the top k nodes with the highest coupling degree; or an iterative greedy approach can be adopted, selecting the node that maximizes the marginal influence gain each time and dynamically updating the coupling degree of neighboring nodes. This iterative filtering operation is then performed from the common node set, gradually eliminating low-influence nodes, ultimately generating a high-influence seed node set. The seed node set, as the initial trigger for information propagation, plays a decisive role in the overall diffusion range and efficiency.

[0104] Specifically, let the set of associated hyperedges of node i be . Each super edge The weight is (The weights can be preset values, determined based on factors such as the importance and strength of the relationship represented by the hyperedge), then the initial coupling degree of node i is... It can be represented as:

[0105]

[0106] Then, based on the initial coupling degree of each node, an iterative filtering operation is performed from the common node set to obtain the seed node set. The seed node set is the starting point for information propagation, and selecting appropriate seed nodes is crucial for the rapid and widespread dissemination of information.

[0107] In one embodiment, an initial coupling threshold can be set, and nodes with an initial coupling degree higher than this threshold can be directly selected into the seed node set. Alternatively, a gradual elimination method can be used, eliminating a certain proportion of nodes with the lowest initial coupling degree each time, until the number of remaining nodes reaches the preset number of seed nodes.

[0108] In addition, during the iterative screening process, the uniformity of node distribution can be considered to avoid seed nodes being too concentrated in certain areas or levels, so as to ensure that information can be spread evenly in the multi-layer system.

[0109] S105. Initialize the initial propagation state of the multi-layer hypergraph structure according to the seed node set, and propagate the information to be propagated within the current hypergraph layer according to the preset intra-layer propagation rules based on the initial propagation state.

[0110] Specifically, a seed node set can be used as the foundation, marking all nodes in the seed node set as "activated" to serve as the initial propagation state for the multi-layered hypergraph structure. Subsequently, a starting hyperlayer (such as layer 1) is selected, and the information diffusion process is initiated according to preset intra-layer propagation rules. Intra-layer propagation rules can be based on an improved independent cascading model, a linear threshold model, or other custom mechanisms. For example: activated nodes send activation signals to inactive nodes through their associated hyperedges; once a hyperedge is activated, it exerts influence on all nodes connected to it; nodes are activated based on whether the cumulative received influence exceeds a preset threshold (or by probability); propagation continues until no new activated nodes are added to the current layer, at which point the propagation of that layer is considered complete.

[0111] Specifically, the initial propagation state can be represented by a matrix or a similar data structure, where each element represents whether the corresponding node is activated at a certain moment (i.e. whether it has received the information to be propagated).

[0112] Nodes in the seed node set are marked as active, and other nodes are marked as inactive. For example, in a multi-layered hypergraph structure with N nodes, a vector S of length N can be created, where S... i=1, indicating that node i is activated, S i =0 indicates that node i is not activated.

[0113] Then, the information to be propagated is propagated within the current superlayer according to the initial propagation state and a preset intra-layer propagation rule. The intra-layer propagation rule describes how information propagates from one node to other nodes within the same superlayer, and can be based on the node's coupling degree and the weight of the hyperedge. For each activated node, its associated hyperedge set is traversed. For each associated hyperedge, if there are inactive nodes on that hyperedge, these inactive nodes are activated with a preset probability. This probability can be adjusted based on factors such as the node's coupling degree and the hyperedge weight; nodes with higher coupling degrees and larger hyperedge weights have a greater probability of activating other nodes.

[0114] In one embodiment, the multi-layer hypergraph structure corresponding to the multi-layer system can be: The superlayer is... The set of public nodes is The set of hyperedges is All layers l share the same common set of nodes. The hyperedge set has different layers; the l-th layer of the hyperedge can be represented as... Hyperedges can connect any number of nodes and are used to characterize group interaction relationships.

[0115] At level l, node v i The set of associated hyperedges (i.e., "v") i (Which hyperedges belong to?) is: In the l-th layer, the hyperedge The set of member nodes is: .

[0116] In the initial propagation state, all hyperedges are initially inactive. The seed node set S⊆V, where seed nodes are active in all layers, and all other non-seed nodes are inactive.

[0117] The propagation rules within a layer can be:

[0118] During the propagation process at layer l, for each node (or seed node) activated in the previous time step... Find its associated set of superedges .

[0119] Next, for the hyperedges in the set of associated hyperedges that have not yet been activated. Node v i It can be based on a preset independent probability p v Try to activate .

[0120] Then, for the successfully activated superedges Find the set of nodes it contains. Each hyperedge is then assigned a preset independent probability p. e Try to activate its inactive nodes.

[0121] If a node is successfully activated, it will be marked as active in all superlayers, indicating the collaborative propagation of group information across multiple layers.

[0122] S106. When it is detected that the information to be propagated has been propagated in the current superlayer, the information to be propagated will be moved to the next superlayer according to the preset cross-layer propagation rules for intra-layer propagation until no new active nodes are added.

[0123] Specifically, when it is detected that the propagation process of the current superlayer has converged, the activation state can be passed to the next superlayer (e.g., jumping from layer l to layer l+1) according to the preset cross-layer propagation rules. The preset cross-layer propagation rules may include:

[0124] Cross-layer synchronization mechanism: Once any node is activated for the first time in any layer, it is immediately synchronized to the active state in all its layers to ensure cross-layer state consistency.

[0125] Layered loop strategy: Iterate through each layer in a fixed order to achieve time-controlled inter-layer linkage.

[0126] Optionally, in one embodiment, the influence of the transmission can be dynamically adjusted based on the interlayer coupling strength.

[0127] By repeatedly executing the "intra-layer propagation-cross-layer jump" loop until a complete round of propagation is completed in the entire multi-layer hypergraph structure without any newly added activated nodes, the propagation process can be determined to have terminated, and the final set of activated nodes, propagation path, coverage area, and key propagation bottlenecks can be output as analysis results.

[0128] In other words, cross-layer propagation can proceed when the information to be propagated has been completed in the current superlayer, i.e., no new nodes are activated in the current superlayer. Specifically, this can be determined by periodically checking whether the number of inactive nodes no longer changes within a preset time (such as 1 hour, 2 hours, 6 hours, or 2 days, etc., without specific restrictions here).

[0129] Next, the information to be propagated is moved to the next superlayer according to the preset cross-layer propagation rules for intra-layer propagation. The cross-layer propagation rules describe how information is transferred between different superlayers. One possible cross-layer propagation rule is that for nodes that are activated in the current superlayer, their related nodes in other superlayers are found (these related nodes can be determined by a common node set). If these related nodes are not activated in the next superlayer, they are activated with a preset probability, and the information to be propagated is introduced into the next superlayer for intra-layer propagation.

[0130] For example, in a social network, if a user is activated in an online social superlayer, then cross-layer propagation can be carried out based on the user's associated nodes in the offline social superlayer (such as friends who participate in the same activities).

[0131] Then, the process of intra-layer and cross-layer propagation is repeated, continuously transferring the information to be propagated between each superlayer until no new activated nodes are added. At this point, the information to be propagated has achieved maximum propagation in the multi-layer system, all potentially activated nodes have been activated, and the propagation process ends.

[0132] This embodiment provides a method for multi-layer signal interaction propagation in a multi-layer system. First, basic information of the multi-layer complex system and information to be propagated are acquired. Then, a multi-layer hypergraph structure is constructed based on the basic information, where the multi-layer hypergraph structure contains multiple hyperlayers. Each hyperlayer shares a common node set and its own hyperedge set. The common node set contains multiple nodes, and the hyperedge set contains multiple hyperedges. Next, based on the common node set and hyperedge set, the associated hyperedge set of each node in the common node set is determined. Then, the initial coupling degree of each node in the common node set is calculated based on the associated hyperedge set of each node, and an iterative filtering operation is performed on the common node set based on the initial coupling degree of each node to obtain a seed node set. Then, the initial propagation state of the multi-layer hypergraph structure is initialized based on the seed node set, and the information to be propagated is propagated within the current hyperlayer according to a preset intra-layer propagation rule based on the initial propagation state. Finally, when it is detected that the information to be propagated has completed propagation in the current hyperlayer, the information to be propagated is moved to the next hyperlayer according to a preset cross-layer propagation rule for intra-layer propagation, until no new active nodes are added.

[0133] The following technical effects were achieved: By acquiring basic information and information to be propagated from a multi-layered complex system, a multi-layered hypergraph structure was constructed, the set of associated hyperedges of nodes was determined, the initial coupling degree was calculated, and a seed node set was selected. Then, intra-layer and cross-layer propagation were performed, realizing the efficient and accurate propagation of information to be propagated in the multi-layered system. It fully considers the complex structure of the multi-layered system and the complex relationships between nodes, possessing strong adaptability and practicality, and providing an effective solution to the information propagation problem in multi-layered systems. By constructing a multi-layered hypergraph structure and integrating high-order interaction modeling and cross-layered collaborative propagation mechanisms, the accuracy of characterizing signal diffusion behavior and the temporal controllability in complex systems are significantly improved. It can be widely applied to scenarios such as fault cascading prediction, critical node identification, information propagation simulation, and infrastructure resilience analysis.

[0134] In one possible implementation, determining the associated hyperedge set of each node in the common node set based on the common node set and the hyperedge set includes: determining the hyperedge to which each node belongs in each hyperlayer as the associated hyperedge set of each node based on the common node set.

[0135] Specifically, for each node v in the common node set, all hyperlayers in the multi-layer hypergraph structure are traversed. For the l-th hyperlayer (where l = 1, 2, ..., L, and L is the total number of hyperlayers), it is checked whether there is a hyperedge containing node v in the hyperedge set of this layer. If so, all these hyperedges containing node v are added to the association record of node v. After traversing all L hyperlayers, all the hyperedges to which node v belongs in each layer are summarized to form its complete set of associated hyperedges.

[0136] In this way, each node is given a cross-layer, global high-order interactive view. This set of associated hyperedges not only reflects the group collaboration relationship of the node in a single layer, but also reflects its cross-layer coupling breadth in a multi-layer system, providing a key data foundation for subsequent calculation of the initial coupling degree of nodes, analysis of propagation potential, and selection of seed nodes.

[0137] In one possible implementation, the initial coupling degree of each node in the common node set is calculated based on the set of associated hyperedges of each node, including: determining the set of neighboring nodes that each node in the common node set is associated with through hyperedges in the corresponding superlayer based on the set of associated hyperedges of each node; and determining the total number of non-repeating neighboring nodes that each node in the common node set is associated with in the corresponding superlayer based on the common node set and the set of neighboring nodes that each node is associated with through hyperedges in the corresponding superlayer, so as to use the total number of non-repeating neighboring nodes as the initial coupling degree of the corresponding node.

[0138] Specifically, for any node v in the common node set, based on its associated hyperedge set (i.e. all hyperedges in which node v participates in all hyperlayers), traverse each hyperedge in the associated hyperedge set. For each hyperedge e, extract all other nodes except node v as the neighbor nodes that node v is associated with in the corresponding hyperlayer through the hyperedge.

[0139] Summarize all neighbor nodes that node v is connected to through its associated hyperedges in all hyperlayers, and remove duplicates to obtain the set of non-duplicate neighbor nodes of node v in the entire multi-layer hypergraph structure.

[0140] Then, the total number of nodes in the set of non-repeating neighbor nodes can be directly defined as the initial coupling degree of node v.

[0141] The initial coupling degree reflects the number of unique entities that node v can directly influence in a multi-layer system through higher-order hyperedges, comprehensively demonstrating its breadth and pivotal role in intra-layer group interactions and cross-layer connections. Calculating the initial coupling degree of each node in the common node set by using the set of associated hyperedges of each node can provide accurate node connection information for subsequent multi-layer signal interaction propagation.

[0142] In one possible implementation, the initial coupling degree is calculated using the following formula:

[0143]

[0144] In the formula, C(v) represents the initial coupling degree of node v. Let represent the set of neighboring nodes that node v is connected to via hyperedges at level l, where is the set of neighboring nodes that node v is connected to via hyperedges at level l. The set of neighboring nodes refers to all other nodes that co-occur with node v in at least one hyperedge, excluding node v itself. This represents the union of the sets of neighboring nodes of node v in L superlayers, where L represents the total number of superlayers in the multi-layered hypergraph structure. This means performing a union operation on the set of neighbor nodes of node v in all L superlayers to eliminate duplicate nodes across layers, thereby obtaining the complete set of its non-duplicate neighbor nodes in the entire multilayer system. The cardinality of the set (i.e., the number of elements) is represented by C(v), so the value of C(v) is equal to the total number of unique neighbor nodes in the union set.

[0145] This calculation method effectively integrates the high-order interaction information of nodes in each layer, avoids duplicate counting caused by repeated connections across layers, and truly reflects the global coupling breadth of node v in a multi-layer complex system, providing a simple and effective quantitative indicator for subsequent seed node selection.

[0146] In one possible implementation, an iterative filtering operation is performed from the common node set based on the initial coupling degree of each node to obtain a seed node set. This includes: sorting the nodes in the common node set according to their initial coupling degree, and selecting the node with the highest initial coupling degree as the seed node; determining whether other nodes in the common node set have no shared hyperedges with the seed node in any hyperlayer, and if so, keeping the initial coupling degree of the node unchanged, otherwise updating the state of all neighboring nodes of the seed node; determining the number of seed nodes associated with each node based on the updated state of each node; determining the updated adaptive coupling degree of each node based on its initial coupling degree and the number of associated seed nodes, and then jumping to the step of sorting the nodes in the common node set according to their initial coupling degree, until a specified number of seed nodes are selected; and forming a seed node set from all selected seed nodes.

[0147] Specifically, firstly, it can be based on the initial coupling degree of each node. Sort all nodes in the common node set in descending order.

[0148] Then select the node with the largest initial coupling degree in the current sort that has not yet been selected as a seed node as the new seed node in this round, and add it to the seed node set S.

[0149] Subsequently, for the remaining nodes in the common node set other than the selected seed, it is determined whether they have no common hyperedge with the newly added seed node in all hyperlayers.

[0150] If a node v and the seed node share at least one common hyperedge in any hyperlayer (i.e., they are connected by the same hyperedge in that layer), then node v is considered a neighbor node of the seed node.

[0151] Conversely, if other nodes in the common node set have no common hyperedge with the seed node in all L hyperlayers, they are considered unrelated, and their state remains unchanged, that is, the initial coupling degree of the node remains unchanged.

[0152] For all nodes identified as neighbors, update their state information, specifically including: recording the number of seed nodes S(v) that it has been associated with, that is, the total number of seed nodes that have been selected into the seed node set S and have cross-layer or intra-layer hyperedge connections with node v.

[0153] Furthermore, based on the original initial coupling degree Given the current number of associated seeds S(v), the formula for determining the adaptive coupling degree of each node after the update is as follows:

[0154]

[0155] In the formula, This represents the adaptive coupling degree of node v after the update. S(v) represents the adaptive coupling degree or initial coupling degree of node v before the update, and S(v) represents the number of associated seed nodes.

[0156] After completing the adaptive coupling degree update, return to the sorting step: reorder the common node set according to the current adaptive coupling degree of each node (which is the initial coupling degree in the first iteration), and repeat the above seed selection, neighbor identification, state update and coupling degree adjustment process until the number of selected seed nodes reaches the preset threshold K (i.e., |S|=K).

[0157] Finally, all selected seed nodes are aggregated to form a seed node set S.

[0158] This iterative selection mechanism effectively selects high-influence and complementary seed nodes in a multi-layer system by introducing an adaptive coupling degree decay strategy and cross-layer neighbor relationship determination. While ensuring the breadth of propagation, it significantly reduces the overlap of influence between nodes and improves the overall influence maximization effect.

[0159] In one possible implementation, initializing the initial propagation state of the multi-layer hypergraph structure based on the seed node set includes: setting the initial state of all hyperedges in the multi-layer hypergraph structure to an inactive state; setting all nodes in the seed node set of the multi-layer hypergraph structure to an active state; and setting all nodes in the common node set but not in the seed node set of the multi-layer hypergraph structure to an inactive state.

[0160] Specifically, firstly, the initial state of all hyperedges in the multi-layer hypergraph structure (regardless of which hyperlayer they belong to) can be uniformly set to an inactive state, indicating that no signal has been transmitted through the hyperedges or triggered group interaction before the propagation process begins.

[0161] Secondly, the state of all nodes in the common node set is initialized, including: setting the state of nodes belonging to the seed node set S to active state, serving as the initial source points for the information to be propagated; and setting the state of the remaining nodes belonging to the common node set but not selected into the seed node set to inactive state, indicating that they have not yet received the propagation signal.

[0162] Through the above initialization operations, the entire multi-layer hypergraph structure has a clear initial activation distribution at the start of propagation: only the seed node is active, while the remaining nodes and all hyperedges are in a silent state. This setting provides a clear and controllable starting point for subsequent layer-by-layer propagation and cross-layer jumps, ensuring that the propagation process strictly starts from the high-influence seed and gradually spreads according to preset rules, thereby guaranteeing the accuracy and timing consistency of the simulation results.

[0163] This application embodiment can divide an electronic device or main control device into functional modules according to the above method examples. For example, each function can be divided into its own functional modules, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional module. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division; in actual implementation, there may be other division methods.

[0164] Figure 2 This is a schematic diagram of a multi-layer signal interaction propagation device for a multi-layer system, provided as an embodiment of this application. Figure 2 As shown, the device includes: an acquisition module 210; a hypergraph structure construction module 220; an association module 230; a calculation module 240; and an interactive propagation module 250.

[0165] The acquisition module 210 is used to acquire basic information and information to be disseminated from a multi-layered complex system.

[0166] The hypergraph structure construction module 220 is used to construct a multi-layer hypergraph structure based on the basic information of a multi-layer complex system. The multi-layer hypergraph structure contains multiple hyperlayers, and each hyperlayer shares a common set of nodes and its own set of hyperedges. The common set of nodes contains multiple nodes, and the set of hyperedges contains multiple hyperedges.

[0167] The association module 230 is used to determine the associated superedge set of each node in the common node set based on the common node set and the superedge set.

[0168] The calculation module 240 is used to calculate the initial coupling degree of each node in the common node set based on the set of associated hyperedges of each node, and to perform an iterative filtering operation from the common node set based on the initial coupling degree of each node to obtain the seed node set.

[0169] The interactive propagation module 250 is used to initialize the initial propagation state of the multi-layer hypergraph structure according to the seed node set, and to propagate the information to be propagated within the current hypergraph layer according to the preset intra-layer propagation rules based on the initial propagation state.

[0170] The interactive propagation module 250 is also used to, when it is detected that the information to be propagated has been propagated in the current superlayer, jump to the next superlayer according to the preset cross-layer propagation rules for intra-layer propagation, until no new active nodes are added.

[0171] In one possible implementation, the association module 230 is further configured to determine, based on the common node set, the hyperedge to which each node belongs in each hyperlayer as the associated hyperedge set of each node.

[0172] In one possible implementation, the calculation module 240 is further configured to determine, based on the set of associated hyperedges of each node, the set of neighboring nodes that each node in the common node set is associated with through hyperedges in the corresponding hyperlayer.

[0173] The calculation module 240 is also used to determine the total number of non-repeating neighbor nodes associated with each node in the common node set in the corresponding superlayer based on the common node set and the set of neighbor nodes associated with each node in the corresponding superlayer through hyperedges, so as to use the total number of non-repeating neighbor nodes as the initial coupling degree of the corresponding node.

[0174] In one possible implementation, the formula for calculating the initial coupling degree in the calculation module 240 is:

[0175]

[0176] In the formula, C(v) represents the initial coupling degree of node v. Let represent the set of neighboring nodes that node v is connected to via hyperedges at level l, where is the set of neighboring nodes that node v is connected to via hyperedges at level l. , This represents the union of the sets of the L neighboring nodes of node v in the superlayer.

[0177] In one possible implementation, the calculation module 240 is further configured to sort the nodes in the common node set according to the initial coupling degree of each node, and obtain the node with the largest initial coupling degree as the seed node.

[0178] The calculation module 240 is also used to determine, based on the seed node, whether other nodes in the common node set have no common hyperedge with the seed node in each hyperlayer. If so, the initial coupling degree of the node remains unchanged; otherwise, the state of all neighboring nodes of the seed node is updated.

[0179] The calculation module 240 is also used to determine the number of seed nodes associated with each node based on the updated status of each node.

[0180] The calculation module 240 is also used to determine the updated adaptive coupling degree of each node based on the initial coupling degree of each node and the number of associated seed nodes, and jump to the step of sorting the nodes in the common node set according to the initial coupling degree of each node until a specified number of seed nodes are selected.

[0181] The calculation module 240 is also used to form a seed node set from all selected seed nodes.

[0182] In one possible implementation, in the calculation module 240, the formula for determining the updated adaptive coupling degree of each node, based on the initial coupling degree of each node and the number of associated seed nodes, is as follows:

[0183]

[0184] In the formula, This represents the adaptive coupling degree of node v after the update. S(v) represents the adaptive coupling degree or initial coupling degree of node v before the update, and S(v) represents the number of associated seed nodes.

[0185] In one possible implementation, the interaction propagation module 250 is also configured to set the initial state of all hyperedges in the multi-layer hypergraph structure to an inactive state.

[0186] The interactive propagation module 250 is also used to set all nodes in the seed node set of the multi-layer hypergraph structure to an active state, and to set all nodes in the common node set but not in the seed node set of the multi-layer hypergraph structure to an inactive state.

[0187] This embodiment provides a multi-layer signal interaction propagation device for a multi-layer system, which can execute a multi-layer signal interaction propagation method for a multi-layer system as described in the above embodiment. Its implementation principle and technical effect are similar, and will not be repeated here.

[0188] In the aforementioned specific implementation of a multi-layer signal interaction propagation device for a multi-layer system, each module can be implemented as a processor. The processor can execute computer execution instructions stored in the memory, causing the processor to execute the aforementioned multi-layer signal interaction propagation method for a multi-layer system.

[0189] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 3 As shown, the electronic device includes at least one processor 310 and a memory 320. The electronic device also includes a communication component 330. The processor 310, memory 320, and communication component 330 are connected via a bus 340.

[0190] In the specific implementation process, at least one processor 310 executes computer execution instructions stored in memory 320, causing at least one processor 310 to execute a multi-layer signal interaction propagation method for a multi-layer system as executed on the electronic device side.

[0191] The specific implementation process of processor 310 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0192] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0193] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage.

[0194] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0195] The above description of the functions implemented by electronic devices and main control devices has introduced the solutions provided by the embodiments of the present invention. It is understood that, in order to implement the above functions, the electronic device or main control device includes hardware structures and / or software modules corresponding to the execution of each function. By combining the units and algorithm steps of the various examples described in the embodiments of the present invention, the embodiments of the present invention can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the technical solutions of the embodiments of the present invention.

[0196] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the above-described method for multi-layer signal interaction and propagation in a multi-layer system.

[0197] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0198] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in an electronic device or a host device.

[0199] This application also provides a computer program product, which includes a computer program stored in a readable storage medium. At least one processor of an electronic device can read the computer program from the readable storage medium, and the at least one processor executes the computer program to cause the electronic device to perform the solution provided in the above embodiments.

[0200] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disk, or optical disk.

[0201] The technical solutions of this application have been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it is readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. The above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for multi-layer signal interaction propagation in a multi-layer system, characterized in that, include: To acquire basic information and information to be disseminated from a multi-layered complex system; A multi-layer hypergraph structure is constructed based on the basic information of the multi-layer complex system. The multi-layer hypergraph structure contains multiple hyperlayers. Each hyperlayer shares a common set of nodes and its own set of hyperedges. The common set of nodes contains multiple nodes, and the set of hyperedges contains multiple hyperedges. Based on the common node set and the superedge set, determine the associated superedge set of each node in the common node set; The initial coupling degree of each node in the common node set is calculated based on the set of associated hyperedges of each node, and the seed node set is obtained by iteratively filtering the common node set based on the initial coupling degree of each node. The initial propagation state of the multi-layer hypergraph structure is initialized according to the seed node set, and the information to be propagated is propagated within the current hypergraph layer according to the initial propagation state and a preset intra-layer propagation rule. When it is detected that the information to be propagated has been propagated in the current superlayer, the information to be propagated will be moved to the next superlayer according to the preset cross-layer propagation rules for intra-layer propagation until no new active nodes are added. The step of calculating the initial coupling degree of each node in the common node set based on the set of associated hyperedges of each node includes: Based on the set of associated hyperedges of each node, determine the set of neighboring nodes that each node in the common node set is associated with through hyperedges in the corresponding hyperlayer; Based on the common node set and the set of neighbor nodes that each node is associated with through a hyperedge in the corresponding superlayer, determine the total number of non-repeating neighbor nodes that each node in the common node set is associated with in the corresponding superlayer, so as to use the total number of non-repeating neighbor nodes as the initial coupling degree of the corresponding node. The formula for calculating the initial coupling degree is: In the formula, C(v) represents the initial coupling degree of node v. Let represent the set of neighboring nodes that node v is connected to via hyperedges at level l, where is the set of neighboring nodes that node v is connected to via hyperedges at level l. , This represents taking the union of the sets of the L neighboring nodes of node v across the L superlayers. It is a set of superedges; Let be a hyperedge in the l-th layer hypergraph.

2. The method according to claim 1, characterized in that, The step of determining the set of associated superedges for each node in the set of common nodes based on the set of common nodes and the set of superedges includes: Based on the common node set, the hyperedge to which each node belongs in each hyperlayer is determined as the associated hyperedge set of each node.

3. The method according to claim 1, characterized in that, The step of iteratively filtering the common node set based on the initial coupling degree of each node to obtain the seed node set includes: The nodes in the common node set are sorted according to their initial coupling degree, and the node with the highest initial coupling degree is selected as the seed node. Based on the seed node, determine whether other nodes in the common node set have no common hyperedge with the seed node in any hyperlayer. If so, keep the initial coupling degree of the other nodes unchanged; otherwise, update the state of all neighboring nodes of the seed node. Based on the updated status of each node, determine the number of seed nodes associated with each node; Based on the initial coupling degree of each node and the number of associated seed nodes, the updated adaptive coupling degree of each node is determined, and the process jumps to the step of sorting the nodes in the common node set according to the initial coupling degree of each node until a specified number of seed nodes are selected. All selected seed nodes are combined into a seed node set.

4. The method according to claim 3, characterized in that, The formula for determining the updated adaptive coupling degree of each node based on the initial coupling degree of each node and the number of associated seed nodes is as follows: In the formula, This represents the adaptive coupling degree of node v after the update. S(v) represents the adaptive coupling degree or initial coupling degree of node v before the update, and S(v) represents the number of associated seed nodes.

5. The method according to claim 1, characterized in that, Initialize the initial propagation state of the multi-layer hypergraph structure based on the seed node set, including: Set all hyperedges in the multi-layer hypergraph structure to an inactive state initially. All nodes in the seed node set of the multi-layered hypergraph structure are set to active state, and all nodes in the common node set but not in the seed node set of the multi-layered hypergraph structure are set to inactive state.

6. A multi-layer signal interaction propagation device for a multi-layer system, characterized in that, include: The acquisition module is used to acquire basic information and information to be disseminated from a multi-layered complex system. The hypergraph structure construction module is used to construct a multi-layer hypergraph structure based on the basic information of the multi-layer complex system. The multi-layer hypergraph structure includes multiple hyperlayers, each hyperlayer sharing a common set of nodes and its own set of hyperedges. The common set of nodes contains multiple nodes, and the set of hyperedges contains multiple hyperedges. The association module is used to determine the associated superedge set of each node in the common node set based on the common node set and the superedge set. The calculation module is used to calculate the initial coupling degree of each node in the common node set based on the set of associated hyperedges of each node, and to perform an iterative filtering operation from the common node set based on the initial coupling degree of each node to obtain a seed node set. The interactive propagation module is used to initialize the initial propagation state of the multi-layer hypergraph structure according to the seed node set, and propagate the information to be propagated within the current hypergraph layer according to the initial propagation state and a preset intra-layer propagation rule. The interactive propagation module is also used to, when it is detected that the information to be propagated has been propagated in the current superlayer, jump the information to be propagated to the next superlayer according to the preset cross-layer propagation rules for intra-layer propagation, until there are no new activated nodes. Specifically, the calculation module is used for: Based on the set of associated hyperedges of each node, determine the set of neighboring nodes that each node in the common node set is associated with through hyperedges in the corresponding hyperlayer; Based on the common node set and the set of neighbor nodes that each node is associated with through a hyperedge in the corresponding superlayer, determine the total number of non-repeating neighbor nodes that each node in the common node set is associated with in the corresponding superlayer, so as to use the total number of non-repeating neighbor nodes as the initial coupling degree of the corresponding node. The formula for calculating the initial coupling degree is: In the formula, C(v) represents the initial coupling degree of node v. Let represent the set of neighboring nodes that node v is connected to via hyperedges at level l, where is the set of neighboring nodes that node v is connected to via hyperedges at level l. , This represents taking the union of the sets of the L neighboring nodes of node v across the L superlayers. It is a set of superedges; Let be a hyperedge in the l-th layer hypergraph.

7. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 5.

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