Dominant path identification method and system in single-source scenario based on positive feedback of information flow

Abstract

The present invention discloses a method and system for identifying a dominant path in a single-source scenario based on positive feedback of information flow, and relates to the field of computer technology. The present invention performs physical mechanism modeling through electrical network equivalence, Kirchhoff's law, and dynamic evolution driven by positive feedback, and takes physical mechanisms such as flow conservation and dynamic evolution of pipeline carrying capacity as the basis, so that the model has a clear dynamic interpretation. Specifically, the present invention aims at network information propagation, and establishes a dominant path intelligent identification model based on the positive feedback self-ablation mechanism of information flow at the mechanism level. Taking the analysis of information flow penetration law as the entry point, the dominant path when the source node and the target node in the target network perform information interaction can be quickly and accurately identified by means of information flow identification. It plays a core supporting role in constructing a propagation risk warning model, implementing key node intervention strategies, and optimizing the intelligent governance mechanism of social information systems.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method and system for identifying a dominant path in a single-source scenario based on positive feedback of information flow. Background Art

[0002] Dominant path identification involves identifying the paths that primarily carry information during the directional propagation of network information flows. This method is primarily used in areas such as precision marketing within social networks and network structure data optimization.

[0003] Existing dominant path identification methods generally rely on calculating certain metrics to determine the importance of links, and then using local optimal addressing to find the target node. However, these methods suffer from shortcomings such as insufficient mechanism modeling, neglect of weak connections, and complex interaction modeling.

[0004] Therefore, the existing technology still needs to be improved and developed. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to address the above-mentioned defects of the existing technology and provide a method and system for identifying the dominant path in a single-source scenario based on positive feedback of information flow. The purpose is to solve the problem that the existing dominant path identification method obtains the importance of the link by calculating indicators and identifies the dominant path through local optimal addressing, which has insufficient mechanism modeling.

[0006] The technical solutions adopted by the present invention to solve the problem are as follows:

[0007] In a first aspect, an embodiment of the present invention provides a method for identifying a dominant path in a single-source scenario based on positive feedback of information flow, the method comprising:

[0008] Determine a pair of source nodes and target nodes that interact with each other in a target network, and input the network structure data of the target network into a dominant path intelligent identification model built based on an information flow positive feedback self-ablation mechanism;

[0009] Determining the dominant path when the source node and the target node perform information interaction by using the dominant path intelligent identification model;

[0010] The dominant path intelligent identification model is used to:

[0011] Based on the network structure data, the links used for information transmission in the target network are equivalent to pipes that carry traffic during information transmission in the electrical network, and the traffic flowing through each node in the target network complies with the conservation law, and the traffic flowing through each link complies with Kirchhoff's law;

[0012] The flow distribution and flow carrying capacity of the pipeline between the source node and the target node are iteratively updated by an evolutionary algorithm until the evolution reaches a steady state, and the dominant path is determined according to the flow distribution results or flow carrying capacity of each pipeline after the evolution reaches a steady state.

[0013] In one embodiment, the type of the target network includes a social network.

[0014] In one embodiment, the flow carrying capacity of each pipeline is a non-fixed value that changes with time, and the flow carrying capacity of the pipeline changes with the flow in the pipeline based on a positive feedback mechanism.

[0015] In one embodiment, the evolutionary algorithm is used to:

[0016] The first step is to trigger the current round of traffic allocation based on the traffic carrying capacity of each pipeline between the source node and the target node;

[0017] In the second step, the flow carrying capacity of each pipeline between the source node and the target node is adjusted based on the flow distribution results of the current round and a pre-established differential equation; wherein the differential equation is generated based on the dynamic evolution model established by the positive feedback mechanism, and the differential equation is used to describe the dynamic evolution of the flow carrying capacity of the pipeline with changes in flow rate;

[0018] The third step is to determine whether the evolution has reached a steady state. If not, the first step is continued until the evolution reaches a steady state and the iteration is stopped.

[0019] In one embodiment, the differential equation includes a first parameter and a second parameter; the first parameter is used to adjust the steady-state value of the final pipeline convergence; the second parameter is used to adjust the pipeline convergence speed.

[0020] In one embodiment, adjusting the flow carrying capacity of each pipeline between the source node and the target node according to the flow distribution result and a pre-established differential equation includes:

[0021] For each pipeline between the source node and the target node, if the calculation result of the differential equation corresponding to the pipeline is a negative number, indicating that the flow rate in the pipeline is less than the maximum flow carrying capacity, then reducing the flow carrying capacity of the pipeline;

[0022] If the calculation result of the differential equation corresponding to the pipeline is a positive number, it means that the flow rate in the pipeline is greater than the maximum flow carrying capacity, then the flow carrying capacity of the pipeline is increased;

[0023] If the flow carrying capacity of the pipeline is zero after adjustment, but the evolution has not reached a steady state, the pipeline will be retained, but the transmission activities of the pipeline will be suspended until the flow carrying capacity of the pipeline increases in the subsequent evolution process, and then the transmission activities of the pipeline will be resumed.

[0024] In one embodiment, determining the dominant path according to the flow distribution results or flow carrying capacity of each pipeline after the evolution reaches a steady state includes:

[0025] Selecting a number of target links based on the traffic distribution results or traffic carrying capacity of each pipeline after the evolution reaches a steady state and a preset threshold, and making the path composed of all the target links a connected graph;

[0026] The dominant path is determined according to all the target links.

[0027] In a second aspect, an embodiment of the present invention further provides a dominant path identification system in a single-source scenario based on positive feedback of information flow, the system comprising:

[0028] A data input module is used to determine a pair of source nodes and target nodes that interact with each other in a target network, and input the network structure data of the target network into a dominant path intelligent identification model built based on the information flow positive feedback self-ablation mechanism;

[0029] The dominant path intelligent identification model is used to, based on the network structure data, equate the links used for information transmission in the target network to pipes that carry traffic during information transmission in the electrical network, and the traffic flowing through each node in the target network complies with the conservation law, and the traffic flowing through each link complies with Kirchhoff's law;

[0030] The flow distribution and flow carrying capacity of the pipeline between the source node and the target node are iteratively updated through an evolutionary algorithm until the evolution reaches a steady state; based on the flow distribution results or flow carrying capacity of each pipeline after the evolution reaches the steady state, the dominant path when the source node and the target node perform information interaction is determined.

[0031] In a third aspect, an embodiment of the present invention further provides a terminal, characterized in that the terminal includes a memory and one or more processors; the memory stores one or more programs; the program includes instructions for executing any of the above-mentioned methods for identifying a dominant path in a single-source scenario based on positive feedback of information flow; and the processor is used to execute the program.

[0032] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium on which multiple instructions are stored, characterized in that the instructions are suitable for being loaded and executed by a processor to implement the steps of the dominant path identification method in a single-source scenario based on positive feedback of information flow as described above.

[0033] Beneficial effects of the present invention: The embodiments of the present invention perform physical mechanism modeling through electrical network equivalence, Kirchhoff's law, and dynamic evolution driven by positive feedback, and based on physical mechanisms such as flow conservation and dynamic evolution of pipeline carrying capacity, the model has a clear dynamic interpretation. It solves the problem of insufficient mechanism modeling in existing dominant path identification methods. Specifically, the present invention aims at network information propagation and establishes a dominant path intelligent identification model based on the positive feedback self-ablation mechanism of information flow at the mechanism level. Taking the analysis of information flow penetration law as the entry point, the dominant path when the source node and the target node in the target network interact with each other is quickly and accurately identified through information flow identification. It plays a core supporting role in constructing a propagation risk warning model, implementing key node intervention strategies, and optimizing the intelligent governance mechanism of social information systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0035] Figure 1 It is a flowchart of a method for identifying a dominant path in a single-source scenario based on positive feedback of information flow provided by an embodiment of the present invention.

[0036] Figure 2 It is a module diagram of a dominant path identification system in a single-source scenario based on positive feedback of information flow provided by an embodiment of the present invention.

[0037] Figure 3 This is a principle block diagram of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The present invention discloses a method and system for identifying a dominant path in a single-source scenario based on positive feedback of information flows. To clarify the objectives, technical solutions, and effects of the present invention, the present invention is further described below with reference to the accompanying drawings and examples. It should be understood that the specific examples described herein are intended only to illustrate the present invention and are not intended to limit the present invention.

[0039] It will be understood by those skilled in the art that, unless expressly stated otherwise, the singular forms "a", "an", "said" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the description of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or wireless couplings. The term "and / or" used herein includes all or any units and all combinations of one or more associated listed items.

[0040] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0041] In response to the above-mentioned defects of the prior art, the present invention provides a method for identifying a dominant path in a single-source scenario based on positive feedback of information flow, the method comprising: determining a pair of source nodes and a target node that perform information interaction in a target network, inputting the network structure data of the target network into a dominant path intelligent identification model constructed based on the positive feedback self-ablation mechanism of information flow; determining the dominant path when the source node and the target node perform information interaction through the dominant path intelligent identification model; the dominant path intelligent identification model is used to: equate the target network to an electrical network based on the network structure data; wherein, the nodes in the target network correspond to the nodes in the electrical network, the links for information transmission in the target network correspond to the pipes that carry traffic during information transmission in the electrical network, and each node conforms to Kirchhoff's law; iteratively updating the traffic distribution and traffic carrying capacity of the pipe between the source node and the target node through an evolutionary algorithm until the evolution reaches a steady state, and determining the dominant path based on the traffic distribution results or traffic carrying capacity of each pipe after the evolution reaches a steady state. The present invention models the physical mechanism through electrical network equivalence, Kirchhoff's law, and dynamic evolution driven by positive feedback, and takes physical mechanisms such as flow conservation and dynamic evolution of pipeline carrying capacity as the basis, so that the model has a clear dynamic interpretation. It solves the problem of insufficient mechanism modeling in existing dominant path identification methods. The present invention aims at network information propagation, and establishes a dominant path intelligent identification model based on the positive feedback self-ablation mechanism of information flow from a mechanism level. Taking the analysis of information flow penetration law as the entry point, the dominant path of information interaction between a pair of source nodes and target nodes in the target network is quickly and accurately identified through information flow identification. In actual application scenarios, the present invention can provide a new solution for finding dominant paths for various networks, and plays a core supporting role in building a communication risk warning model based on network science, implementing key node intervention strategies, and optimizing the intelligent governance mechanism of social information systems.

[0042] like Figure 1 As shown, the method specifically includes the following steps:

[0043] Step S100: determining a pair of source nodes and target nodes performing information exchange in a target network, and inputting network structure data of the target network into a dominant path intelligent identification model constructed based on an information flow positive feedback self-ablation mechanism.

[0044] Specifically, the target network can be any network requiring dominant path identification, such as a social network, the Internet of Things, a computer network, a railway network, a transportation network, and so on. First, the indexes of source and target node pairs in the target network requiring information exchange are determined, thereby obtaining the source and target nodes. A dominant path is composed of the links that carry the majority of information flow during information exchange between the source and target nodes. To quickly and accurately identify the dominant path, this embodiment requires inputting the target network's network structure data into a pre-built dominant path intelligent identification model. In practical application scenarios, network structure data, also referred to as graph structure data, can be generated through code or obtained by reading actual network structure data. Network structure data can be stored in a computer using an adjacency matrix or adjacency table. Information flow permeation exhibits a pattern: for information transmission between a source and target node pair, a small number of reachable paths carry the majority of the information flow, while the majority of paths formed by edges only transport a small portion of the information flow, resulting in significant dominant path behavior. The dominant path intelligent identification model is constructed using an information flow positive feedback self-ablation mechanism, which includes both a positive feedback mechanism and a self-ablation mechanism. The positive feedback mechanism manifests itself in a link's traffic carrying capacity being proportional to the traffic flowing through it: higher traffic means higher traffic carrying capacity, while lower traffic means lower traffic carrying capacity. The self-ablation mechanism is a side effect of the positive feedback mechanism's implementation. During the evolution process based on the positive feedback mechanism, the traffic carrying capacity of some links may gradually decrease to zero. This phenomenon is called self-ablation.

[0045] Step S200: determining the dominant path when the source node and the target node perform information interaction by using the dominant path intelligent recognition model;

[0046] The dominant path intelligent identification model is used to: based on the network structure data, equate the links used for information transmission in the target network to pipes that carry traffic during information transmission in the electrical network, and the traffic flowing through each node in the target network complies with the conservation law, and the traffic flowing through each link complies with Kirchhoff's law; iteratively update the traffic distribution and traffic carrying capacity of the pipe between the source node and the target node through an evolutionary algorithm until the evolution reaches a steady state, and determine the dominant path based on the traffic distribution results or traffic carrying capacity of each pipe after the evolution reaches a steady state.

[0047] Specifically, this embodiment uses a dominant path intelligent identification model to quickly and accurately determine the dominant path along which traffic flows during information transmission between source and destination nodes. This dominant path intelligent identification model primarily involves two components: network structure data equivalence and an evolutionary algorithm.

[0048] To achieve network structure data equivalence in the first part, pipes are introduced to replace links (also called edges) in the target network. The pipe's thickness describes the flow-carrying capacity of the pipe and can adaptively change based on the flow rate. Combined with Kirchhoff's laws, the target network can be equated to an electrical network (i.e., a circuit network or electrical network): With a source node as the input node and a target node as the output node, if a constant flow or current (e.g., a constant flow of 1) is fed into the source node, the entire electrical network will be energized, and traffic will flow into the source node and out of the target node.

[0049] The second part of the evolutionary algorithm uses a positive feedback mechanism to adapt the pipeline's traffic carrying capacity to the traffic flow, updating the pipeline's traffic carrying capacity and triggering a new round of traffic distribution evolution. After evolving to a steady state, the dominant paths in the information flow are identified based on the distribution of traffic or traffic carrying capacity. For example, links with high traffic volume in the target network may be identified as dominant paths.

[0050] For example, first explain the definition of information flow and determine the target network , Indicates the number of nodes contained in the target network, Indicates the number of links in the target network. The link between two nodes is one link. Inject unit traffic at the target node and then Converging outflow, consider all possible path solutions for traffic from the source node to the target node, where, except for the source node and the target node, all other nodes along the path satisfy the inflow equal to the outflow.

[0051] The links in the target network are categorized as scalable pipes, used to carry traffic during information dissemination. Initially, the traffic carrying capacity of all pipes is initialized to 1. Since the traffic amplitude from the source node is also 1, the maximum traffic carrying capacity of the pipes will not exceed 1.

[0052] In order to quantify the flow component in each link, Ohm's law in electrical networks is introduced, that is, the flow between nodes is determined by the power difference between the nodes. As a result, we can get:

[0053] ;

[0054] Where: To pass the link Traffic volume; is a time-varying value, indicating a node and nodes Connected links Traffic carrying capacity, whose value determines the thickness of the link or pipe; It is the pressure difference or public opinion difference between nodes.

[0055] Kirchhoff's law: Traffic flows are conserved at all nodes except the source and destination nodes. Solving the traffic conservation equations for all nodes yields the voltage distribution at each node, and thus the traffic distribution of the link.

[0056] Applying Kirchhoff's law to all nodes in the target network yields:

[0057] ;

[0058] Where: Represents the traffic flowing into the source node and the traffic flowing out of the destination node (or represents the information flow that the source node wants to transmit); Indicates that the source node and directly connected nodes Traffic flowing through the links connected to the nodes (i.e., direct neighbor nodes); Indicates that the target node and directly connected nodes The amount of traffic flowing through the connected links. The traffic flowing into and out of the intermediate nodes is equal and cancels each other out.

[0059] For a target network containing multiple nodes, the above two equations can be written in matrix form:

[0060] ;

[0061] Where: Matrix The off-diagonal elements of are nodes and nodes The conductance of the link between the diagonal elements ; Column vector have , as well as , represents a vector describing the magnitude of the net flow in / out of each node, Indicates The element corresponding to the source node s index, Indicates The corresponding target node The indexed element, Indicates Corresponding to other nodes The element of the index.

[0062] The links in the target network are compared to pipelines, which have a certain traffic carrying capacity. . The flow distribution and flow carrying capacity of each pipeline are iteratively updated through the evolutionary algorithm. At the beginning of the iteration, the flow carrying capacity of each pipeline is initialized, and the flow distribution is triggered. The flow carrying capacity of each pipeline is adjusted according to the flow classification results and the positive feedback mechanism. In subsequent iterations, a new round of flow distribution is triggered based on the new flow carrying capacity of each pipeline, and the flow carrying capacity of each pipeline is adjusted again according to the flow classification results and the positive feedback mechanism until the evolution reaches a steady state.

[0063] In one implementation, the flow carrying capacity of each pipeline is a non-fixed value that changes with time, and the flow carrying capacity of the pipeline changes with the flow in the pipeline based on a positive feedback mechanism.

[0064] Specifically, the positive feedback mechanism of information flow reflects the following: a pipeline's flow carrying capacity is not a fixed value, but a value that changes over time. The trend of change depends on the amount of flow in the pipeline, and this trend follows a positive feedback mechanism: with higher flow, the pipeline's flow carrying capacity increases; with lower flow, the pipeline's flow carrying capacity decreases, leading to a decrease in flow. In a practical application scenario, taking a pipeline as an example, after flow allocation is triggered, if the flow in the pipeline is less than the pipeline's flow carrying capacity, the pipeline's flow carrying capacity will subsequently decrease; if the flow in the pipeline is greater than the pipeline's flow carrying capacity, the pipeline's flow carrying capacity will subsequently increase.

[0065] In one implementation, the evolutionary algorithm is used to:

[0066] The first step is to trigger the current round of traffic allocation based on the traffic carrying capacity of each pipeline between the source node and the target node;

[0067] In the second step, the flow carrying capacity of each pipeline between the source node and the target node is adjusted based on the flow distribution results of the current round and a pre-established differential equation; wherein the differential equation is generated based on the dynamic evolution model established by the positive feedback mechanism, and the differential equation is used to describe the dynamic evolution of the flow carrying capacity of the pipeline with changes in flow rate;

[0068] The third step is to determine whether the evolution has reached a steady state. If not, continue to execute the first step until the evolution reaches a steady state and stop the iteration.

[0069] The flow distribution in this embodiment is based on two parts. One part is to distribute flow based on the flow carrying capacity of the adaptive pipeline, that is, pipelines with large flow carrying capacity will be allocated more flow; the other part is to distribute flow based on Kirchhoff's law, that is, node flow conservation: the total outflow flow of the source node is equal to the total inflow flow of the target node, and the inflow flow of the intermediate node is equal to the outflow flow. This embodiment pre-constructs a dynamic evolution model of the flow carrying capacity of the pipeline, and adjusts the flow carrying capacity of the pipeline through the dynamic evolution model combined with a positive feedback mechanism. In other words, when distributing flow, the multiple pipelines between the source node and the target node each have different flow carrying capacities. The flow carrying capacity of these pipelines is used to determine how the current round of flow will be distributed among the various pipelines. For example, pipelines with large flow carrying capacity may be allocated more flow, while pipelines with small flow carrying capacity may be allocated relatively less flow.

[0070] The results of the differential equations in the dynamic evolution model are derivatives. Initially, the flow capacity of the pipeline is set to a preset value (for example, 1), and the derivative will initially decrease. During the iteration process, as the flow capacity of the pipeline changes, the flow distribution will also change. According to conservation laws, when the flow in some pipelines increases, the flow in others will decrease. Due to the positive feedback mechanism, the flow in some pipelines will continue to increase until it stabilizes, while the flow in others will continue to decrease until it reaches a steady state, or even decrease to zero.

[0071] After a finite number of iterations, the evolution reaches a steady state, meaning that all indicators of the target network are nearly stable and no longer change. Once the evolution reaches a steady state, the links in the pipeline carrying traffic form a connected graph. In this connected graph, for each node, there are links carrying inbound traffic and links carrying outbound traffic. With the exception of the source and destination nodes, traffic does not remain trapped at nodes connected by only one link.

[0072] In one implementation, the differential equation includes a first parameter and a second parameter; the first parameter is used to adjust a steady-state value of the final pipeline convergence; and the second parameter is used to adjust a pipeline convergence speed.

[0073] The dynamic evolution model of the flow carrying capacity of the pipeline can describe the change of the flow carrying capacity or conductance between any two adjacent nodes in the target network over time. The differential equation of the dynamic evolution model can describe the positive feedback mechanism of the flow carrying capacity of the pipeline to adapt to the flow. The differential equation includes two key parameters: the first parameter , used to adjust the steady-state value of the final pipeline convergence, the second parameter Used to adjust the pipeline convergence speed. and The value range must be greater than 1. Determined by the voltage drop on each link.

[0074] Specifically, the differential equation form of the dynamic evolution model of the flow carrying capacity of the pipeline can be expressed as follows:

[0075] ;

[0076] Where, Indicates time Based on differential equations The change value of Indicates the change step size; is a time-varying value, indicating a node and nodes The traffic carrying capacity of the connected links or the thickness of the pipe; Indicates yes of Power, used to control the trend of the differential equation, The parameter represents the parameter that controls the proportion of the dominant path retained in the end.

[0077] In order to make the differential equation solvable and its solution distribution convenient for subsequent setting of thresholds to filter the dominant path, it is necessary to ensure that as well as .when When is a constant, the equation has a steady-state solution:

[0078] ;

[0079] It should be noted that this embodiment does not The steady-state values ​​in this embodiment are not based on theoretical steady-state values, but on simulated steady-state values. The setting of is due to the heterogeneous characteristics of the target network. For example, the functional attributes (such as bridging edges and redundant edges) and load pressures of different links in the target network are significantly different.

[0080] The high-load links connecting the source node and the target node are called dominant paths. In order to effectively distinguish the dynamic behavior of high-load links from low-load links, this embodiment abandons the traditional static parameterization method and introduces time-varying values. The core concept is the dynamic evolution of link voltage difference. The link load in the target network is unevenly distributed, for example, the hub link carries high traffic, while the edge link load is low. Through real-time feedback of voltage differences, the spatiotemporal heterogeneity of link status can be accurately captured, avoiding model distortion caused by fixed parameters.

[0081] The traditional static model is prone to fall into local equilibrium due to the path dependence of path selection (such as over-reliance on strong connections). A positive feedback mechanism is introduced: highly loaded links increase their traffic absorption capacity through capacity expansion, while underloaded links are naturally eliminated. This process mimics the self-organizing regulation in biological systems, such as the decay of ant colony pheromones, to ensure that network resources continuously tilt toward highly loaded links, thus overcoming the local convergence limitations of the greedy algorithm. Furthermore, based on Kirchhoff's laws, the dynamic adjustment of link capacity strictly adheres to traffic conservation constraints. When a link exits the previously identified dominant path due to a decrease in load, the released traffic is redistributed to adjacent links according to the principle of least resistance, triggering a new round of capacity evolution.

[0082] Specifically, The values ​​are designed to be:

[0083] ;

[0084] Where, is the pressure drop on each link after the flow is passed; For the voltage to The mapping process and In the mapping process to the steady-state value, avoid The coefficient that cancels out the effect of .

[0085] In one implementation, adjusting the flow carrying capacity of each pipeline between the source node and the target node according to the flow distribution result and a pre-established differential equation includes:

[0086] For each pipeline between the source node and the target node, if the calculation result of the differential equation corresponding to the pipeline is a negative number, indicating that the flow rate in the pipeline is less than the maximum flow carrying capacity, then reducing the flow carrying capacity of the pipeline;

[0087] If the calculation result of the differential equation corresponding to the pipeline is a positive number, it means that the flow rate in the pipeline is greater than the maximum flow carrying capacity, then the flow carrying capacity of the pipeline is increased;

[0088] If the flow carrying capacity of the pipeline is zero after adjustment, but the evolution has not reached a steady state, the pipeline will be retained, but the transmission activities of the pipeline will be suspended until the flow carrying capacity of the pipeline increases in the subsequent evolution process, and then the transmission activities of the pipeline will be resumed.

[0089] Specifically, the positive feedback mechanism for information flow is based on the fact that the pipe thickness can change based on changes in the flow rate within the pipe. Therefore, the differential equation in this embodiment essentially describes the dynamic evolution of the pipe's flow-carrying capacity as the flow rate changes. The sign of the differential equation's calculation result reflects the correlation between the pipe's flow rate and the flow-carrying capacity. This relationship is amplified through positive feedback: when the flow rate within the pipe exceeds the flow-carrying capacity, the flow-carrying capacity is strengthened, and when the flow rate within the pipe is insufficient, the flow-carrying capacity is weakened, ultimately causing the system to converge toward a state of equilibrium between the flow rate and the flow-carrying capacity.

[0090] Taking a pipeline as an example, when the calculated flow rate for the pipeline is less than its maximum flow carrying capacity, the differential equation yields a negative result, indicating that the pipeline's flow carrying capacity will weaken at the next moment. When the calculated flow rate for the pipeline is greater than its maximum flow carrying capacity, the differential equation yields a positive result, indicating that the pipeline's flow carrying capacity will strengthen at the next moment. The process by which a pipeline's flow carrying capacity gradually decreases to zero is equivalent to a self-ablation process. However, to avoid falling into a local optimum, this embodiment retains pipelines with zero flow carrying capacity and merely suspends their transmission activity until the evolution reaches a steady state. Later, if the local optimum is escaped during the evolution process and the flow carrying capacity of such pipelines increases, transmission activity can resume. In other words, some pipelines will initially be identified as non-dominant links, their flow carrying capacity gradually decreasing to zero. Later, after escaping the local optimum, they will be identified as dominant paths again, and their flow carrying capacity will increase again.

[0091] In one implementation, after iteration, if the derivative calculated by the differential equation is less than 0, the result of the differential equation is directly set to , e represents a natural constant.

[0092] Specifically, when When it is related to the voltage difference on the link, if the flow on the link is very small at a certain moment, the voltage difference is also small accordingly. When the whole The value will become very large, and the derivative calculated by the differential equation will become negative. The setting is in the range of [0,1], so it will be a number in the range of 0 to 1 minus a large number. At this time, the derivative may exceed its own size and become a negative number after one round of iteration. It is illegal to take the square root of a negative number and it is not in line with reality (the flow carrying capacity of the pipeline in reality will not be less than 0). At this time, artificial normalization is required. Therefore, in actual application scenarios, after iteration, if the derivative calculated by the differential equation is less than 0, it is directly set to .

[0093] The core of the information flow self-ablation mechanism is to eliminate redundant pipelines through threshold judgment, ensuring that system resources are concentrated on high-load links. Furthermore, the self-ablation mechanism can also strengthen the positive feedback mechanism: the positive feedback mechanism itself causes high-load links to continuously increase their traffic carrying capacity, while low-load links continuously reduce their traffic carrying capacity. When low-load links cannot meet the minimum survival requirements, they are directly eliminated to prevent them from occupying system resources, allowing traffic to be more concentrated on high-load links, forming a virtuous cycle. For example, if the traffic on some links is consistently less than the maximum traffic carrying capacity of the pipeline, the pipeline's traffic carrying capacity will continue to weaken throughout the evolution process until the pipeline thickness approaches zero, which is equivalent to the pipeline self-ablating due to traffic feedback.

[0094] In one implementation, determining the dominant path according to the flow distribution result or flow carrying capacity of each pipeline after the evolution reaches a steady state includes:

[0095] Selecting a number of target links based on the traffic distribution results or traffic carrying capacity of each pipeline after the evolution reaches a steady state and a preset threshold, and making the path composed of all the target links a connected graph;

[0096] The dominant path is determined according to all the target links.

[0097] In actual application scenarios, when various indicators in the target network tend to be stable, it means that the evolution has reached a steady state, for example The values, node information levels, and calculated voltage or current values ​​all remain essentially unchanged. When the evolution reaches a steady state, the target network is flooded with traffic. However, due to differences in the traffic carrying capacity of each link, some links have high traffic volumes while others have low traffic volumes. Therefore, further screening of dominant paths is necessary. The screening criteria for defining dominant paths include the following three:

[0098] (1) Filtering target links based on traffic volume: A link with a larger traffic volume indicates that the link contributes more to traffic distribution and can be considered as part of the dominant path. In this embodiment, there is a corresponding relationship between edges, links, and pipelines.

[0099] (2) Filter target links based on path connectivity: The path composed of the filtered links should be guaranteed to be a connected subgraph without isolated edges or nodes;

[0100] (3) Screening target links based on consideration of weakly loaded links: Weakly loaded links with low traffic should not be completely ignored. They may still be useful in certain areas of the network (such as some longer paths in the network). Therefore, a balancing mechanism is needed to avoid completely discarding weakly loaded links with low traffic while maintaining path connectivity.

[0101] The target links that ultimately constitute the dominant path are determined through the above three screening criteria. Most of the target links are high-load links, but in order to maintain path connectivity, some low-load links may also exist.

[0102] The dominant path intelligent identification model calculates traffic distribution using Kirchhoff's laws, naturally excluding nodes with only a single link. Furthermore, a graph consisting of links carrying traffic in the network is always a connected graph. In summary, the dominant path intelligent identification model, by incorporating Kirchhoff's laws, naturally meets the first two screening criteria mentioned above. Regarding the third screening criterion, weakly loaded links can facilitate information diffusion in cross-cluster connections. When connections between clusters are sparse and interacting nodes span communities, Kirchhoff's laws and conservation laws ensure that traffic will inevitably flow through these cross-cluster links. In this case, threshold screening can accurately identify weakly loaded links that are valuable. In other words, in cross-cluster connections, if weakly loaded links carry traffic, they can be retained by appropriately setting thresholds.

[0103] For example, when evolution reaches a steady state, a threshold is artificially set, such as . Set the flow rate to be less than or equal to The link with a flow rate of approximately 0 or equal to 0 is considered and disconnected; the link with a flow rate greater than , but the links that do not reach the threshold corresponding to the high-load links are considered to be weak-load links that need to be retained.

[0104] Therefore, this embodiment can identify not only high-load links but also weak-load links that need to be retained through information flow identification: after iterating to a steady state, if there is traffic on the weak-load link, its retention is considered as part of the dominant path; if there is no traffic on the weak-load link, it means that it is not very helpful for information diffusion in information transmission and cannot be considered as part of the dominant path.

[0105] Based on the above embodiments, the present invention also provides a dominant path identification system in a single-source scenario based on positive feedback of information flow, such as Figure 2 As shown, the system includes:

[0106] Data input module 01 is used to determine a pair of source nodes and target nodes that interact with each other in a target network, and input the network structure data of the target network into a dominant path intelligent identification model based on the information flow positive feedback self-ablation mechanism;

[0107] The dominant path intelligent identification model 02 is used to, based on the network structure data, equate the links used for information transmission in the target network to pipes that carry traffic during information transmission in the electrical network, and the traffic flowing through each node in the target network complies with the conservation law, and the traffic flowing through each link complies with Kirchhoff's law;

[0108] The flow distribution and flow carrying capacity of the pipeline between the source node and the target node are iteratively updated through an evolutionary algorithm until the evolution reaches a steady state; based on the flow distribution results or flow carrying capacity of each pipeline after the evolution reaches the steady state, the dominant path when the source node and the target node perform information interaction is determined.

[0109] Based on the above embodiment, the present invention further provides a terminal, whose principle block diagram can be shown as follows: Figure 3 As shown. The terminal includes a processor, a memory, a network interface, and a display screen connected via a system bus. The processor of the terminal is used to provide computing and control capabilities. The memory of the terminal includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the terminal is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a dominant path identification method in a single-source scenario based on positive feedback of information flow is implemented. The display screen of the terminal can be a liquid crystal display or an electronic ink display.

[0110] Those skilled in the art will understand that Figure 3 The principle block diagram shown in the figure is only a block diagram of a partial structure related to the solution of the present invention, and does not constitute a limitation on the terminal to which the solution of the present invention is applied. The specific terminal may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0111] In one implementation, the terminal has one or more programs stored in its memory and is configured to be executed by one or more processors. The one or more programs include instructions for performing a method for identifying a dominant path in a single-source scenario based on positive feedback of information flow.

[0112] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware using a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0113] In summary, the present invention discloses a method and system for identifying a dominant path in a single-source scenario based on positive feedback of information flow, which relates to the field of computer technology. The method includes: determining a pair of source nodes and target nodes that perform information interaction in a target network, inputting the network structure data of the target network into a dominant path intelligent identification model constructed based on the positive feedback self-ablation mechanism of information flow; determining the dominant path when the source node and the target node perform information interaction through the dominant path intelligent identification model; the dominant path intelligent identification model is used to: equate the target network to an electrical network based on the network structure data; wherein the nodes in the target network correspond to the nodes in the electrical network, the links for information transmission in the target network correspond to the pipes that carry traffic during information transmission in the electrical network, and each node complies with Kirchhoff's law; iteratively updating the traffic distribution and traffic carrying capacity of the pipes between the source node and the target node through an evolutionary algorithm until the evolution reaches a steady state, and determining the dominant path based on the traffic distribution results or traffic carrying capacity of each pipe after the evolution reaches a steady state. The present invention models the physical mechanism through electrical network equivalence, Kirchhoff's law, and dynamic evolution driven by positive feedback, and takes physical mechanisms such as flow conservation and dynamic evolution of pipeline carrying capacity as the basis, so that the model has a clear dynamic interpretation. It solves the problem of insufficient mechanism modeling in existing dominant path identification methods. The present invention aims at network information propagation, and establishes a dominant path intelligent identification model based on the positive feedback self-ablation mechanism of information flow from a mechanism level. Taking the analysis of information flow penetration law as the entry point, the dominant path of information interaction between a pair of source nodes and target nodes in the target network is quickly and accurately identified through information flow identification. In actual application scenarios, the present invention can provide a new solution for finding dominant paths for various networks, and plays a core supporting role in building a communication risk warning model based on network science, implementing key node intervention strategies, and optimizing the intelligent governance mechanism of social information systems.

[0114] It should be understood that the application of the present invention is not limited to the above examples. For those skilled in the art, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A method for identifying a dominant path in a single-source scenario based on positive feedback of information flow, characterized in that: The method comprises: A pair of source and destination nodes performing information exchange in a target network is identified, and the network structure data of the target network is input into a dominant path intelligent identification model constructed based on an information flow positive feedback self-ablation mechanism. The information flow positive feedback self-ablation mechanism includes an information flow positive feedback mechanism and a self-ablation mechanism. The positive feedback mechanism is reflected in the fact that the traffic carrying capacity of a link is proportional to the traffic in the link. The self-ablation mechanism is a side effect of the positive feedback mechanism implementation process, and is reflected in the fact that during the evolution process according to the positive feedback mechanism, the traffic carrying capacity of some links will gradually decrease to zero. Determining the dominant path when the source node and the target node perform information interaction by using the dominant path intelligent identification model; The dominant path intelligent identification model is used to: Based on the network structure data, the links used for information transmission in the target network are equivalent to pipes that carry traffic during information transmission in the electrical network, and the traffic flowing through each node in the target network complies with the conservation law, and the traffic flowing through each link complies with Kirchhoff's law; The flow distribution and flow carrying capacity of the pipeline between the source node and the target node are iteratively updated by an evolutionary algorithm until the evolution reaches a steady state, and the dominant path is determined according to the flow distribution results or flow carrying capacity of each pipeline after the evolution reaches a steady state.

2. The method for identifying the dominant path in a single-source scenario based on positive feedback of information flow according to claim 1 is characterized in that: The type of the target network includes a social network.

3. The method for identifying the dominant path in a single-source scenario based on positive feedback of information flow according to claim 1 is characterized in that: The flow carrying capacity of each pipeline is a non-fixed value that changes with time, and the flow carrying capacity of the pipeline changes with the flow in the pipeline based on a positive feedback mechanism.

4. The method for identifying the dominant path in a single-source scenario based on positive feedback of information flow according to claim 3 is characterized in that: The evolutionary algorithm is used to: The first step is to trigger the current round of traffic allocation based on the traffic carrying capacity of each pipeline between the source node and the target node; In the second step, the flow carrying capacity of each pipeline between the source node and the target node is adjusted based on the flow distribution results of the current round and a pre-established differential equation; wherein the differential equation is generated based on the dynamic evolution model established by the positive feedback mechanism, and the differential equation is used to describe the dynamic evolution of the flow carrying capacity of the pipeline with changes in flow rate; The third step is to determine whether the evolution has reached a steady state. If not, continue to execute the first step until the evolution reaches a steady state and stop the iteration.

5. The method for identifying the dominant path in a single-source scenario based on positive feedback of information flow according to claim 4 is characterized in that: The differential equation includes a first parameter and a second parameter; the first parameter is used to adjust the steady-state value of the final pipeline convergence; the second parameter is used to adjust the pipeline convergence speed.

6. The method for identifying the dominant path in a single-source scenario based on positive feedback of information flow according to claim 4 is characterized in that: According to the flow distribution result and the pre-established differential equation, the flow carrying capacity of each pipeline between the source node and the target node is adjusted, including: For each pipeline between the source node and the target node, if the calculation result of the differential equation corresponding to the pipeline is a negative number, indicating that the flow rate in the pipeline is less than the maximum flow carrying capacity, then reducing the flow carrying capacity of the pipeline; If the calculation result of the differential equation corresponding to the pipeline is a positive number, it means that the flow rate in the pipeline is greater than the maximum flow carrying capacity, then the flow carrying capacity of the pipeline is increased; If the flow carrying capacity of the pipeline is zero after adjustment, but the evolution has not reached a steady state, the pipeline will be retained, but the transmission activities of the pipeline will be suspended until the flow carrying capacity of the pipeline increases in the subsequent evolution process, and then the transmission activities of the pipeline will be resumed.

7. The method for identifying the dominant path in a single-source scenario based on positive feedback of information flow according to claim 1 is characterized in that: Determining the dominant path according to the flow distribution results or flow carrying capacity of each pipeline after the evolution reaches a steady state includes: Selecting a number of target links based on the traffic distribution results or traffic carrying capacity of each pipeline after the evolution reaches a steady state and a preset threshold, and making the path composed of all the target links a connected graph; The dominant path is determined according to all the target links.

8. A dominant path identification system in a single-source scenario based on positive feedback of information flow, characterized in that: The system comprises: A data input module is configured to determine a pair of source and target nodes performing information exchange in a target network, and input the target network structure data into a dominant path intelligent identification model constructed based on an information flow positive feedback self-ablation mechanism. The information flow positive feedback self-ablation mechanism includes an information flow positive feedback mechanism and a self-ablation mechanism. The positive feedback mechanism is reflected in the fact that the traffic carrying capacity of a link is proportional to the traffic in the link. The self-ablation mechanism is a side effect of the positive feedback mechanism implementation process, and is reflected in the fact that during the evolution process according to the positive feedback mechanism, the traffic carrying capacity of some links gradually decreases to zero. The dominant path intelligent identification model is used to, based on the network structure data, equate the links used for information transmission in the target network to pipes that carry traffic during information transmission in the electrical network, and the traffic flowing through each node in the target network complies with the conservation law, and the traffic flowing through each link complies with Kirchhoff's law; The flow distribution and flow carrying capacity of the pipeline between the source node and the target node are iteratively updated through an evolutionary algorithm until the evolution reaches a steady state; based on the flow distribution results or flow carrying capacity of each pipeline after the evolution reaches the steady state, the dominant path when the source node and the target node perform information interaction is determined.

9. A terminal, characterized in that: The terminal includes a memory and one or more processors; the memory stores one or more programs; the program includes instructions for executing the dominant path identification method in a single-source scenario based on positive feedback of information flow as described in any one of claims 1-7; and the processor is used to execute the program.

10. A computer-readable storage medium having a plurality of instructions stored thereon, characterized in that: The instructions are suitable for being loaded and executed by a processor to implement the steps of the method for identifying a dominant path in a single-source scenario based on positive feedback of information flow as described in any one of claims 1 to 7.

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