Dominant path identification method and system in multi-source scenarios based on perturbation response model

Abstract

The present invention discloses a method and system for identifying a dominant path in a multi-source scenario based on a perturbation response model, and relates to the field of computer technology. The present invention dynamically analyzes the impact of link state changes on the steady state of the system based on a perturbation response method, rather than relying on static indicators, and is closer to the real interaction mechanism of the network, thereby improving the accuracy of mechanism modeling. By traversing the link state, the actual contribution of each link to the target node response is quantified, avoiding ignoring the complex interactions of weak connections and multi-link collaboration. Through the process of the system re-evolving to a steady state after the disturbance, the traffic distribution and competition at the shared node are naturally modeled, rather than relying on local optimal addressing, thereby improving the global rationality of dominant path identification and solving the problem of multi-source target resource competition. The method of the present invention is applicable to all types of networks, and plays a core supporting role in constructing a communication risk warning model based on network science, 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 multi-source scenario based on a perturbation response model. Background Art

[0002] Multi-source-destination scenarios involve multiple pairs of source and destination nodes. Identifying dominant paths in multi-source-destination scenarios involves identifying the paths that primarily carry information during the directional propagation of network information flows, when multiple source and destination nodes are interacting with each other. This technology is primarily used in areas such as public health alerts and network structure optimization within social networks.

[0003] Existing methods for identifying dominant paths generally calculate link importance by calculating certain metrics and then use local optimal addressing to find the target node. However, these methods suffer from shortcomings such as insufficient mechanistic modeling, neglect of weak links in dominant paths, and complex interaction modeling. Furthermore, identifying dominant paths in multi-source-target scenarios presents modeling difficulties due to the potential for resource contention at shared nodes.

[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 provide a method and system for identifying the dominant path in a multi-source scenario based on a perturbation response model in response to the above-mentioned defects of the prior art, aiming to solve the problems in the prior art of the dominant path identification method, such as insufficient mechanism modeling and easy resource competition at shared nodes.

[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 multi-source scenario based on a perturbation response model, the method comprising:

[0008] Determining node indexes of multiple pairs of source nodes and target nodes in a target network, and inputting graph structure data of the target network and the node indexes into a perturbation response model;

[0009] Determining the dominant path in a multi-node interaction scenario using the perturbation response model;

[0010] The perturbation response model is used to: for each link in the target network, apply perturbations to each source node in turn under the two link states of normal and stopped propagation of the link, and make the target network evolve from an initial steady state to a new steady state, thereby obtaining two new steady states; determine the traffic carried by the link during the information propagation process of different source nodes based on the response amplitude of each target node in the initial steady state and the two new steady states, and obtain the link traffic carrying distribution data of each source node; determine the dominant path based on the link traffic carrying distribution data corresponding to each source node.

[0011] In one embodiment, a disturbance is applied to each source node in sequence under two link states, namely, normal and stopped propagation of the link, and the target network is caused to re-evolve from an initial steady state to a new steady state, thereby obtaining two new steady states, including:

[0012] In each link state, applying a disturbance of a specific amplitude to one of the source nodes of the target network in an initial steady state, so that the target network enters an unsteady state;

[0013] By solving the ordinary differential equation corresponding to the information flow dynamics equation, the target network in the non-steady state is re-evolved to a new steady state; the information flow dynamics equation is used to describe the node interaction behavior in different scenarios.

[0014] In one embodiment, solving the ordinary differential equation corresponding to the information flow dynamics equation includes:

[0015] The ordinary differential equation corresponding to the information flow dynamics equation is solved by the Runge-Kutta algorithm.

[0016] In one embodiment, determining the traffic carried by the link during information propagation at different source nodes based on the response amplitude of each target node in the initial steady state and the two new steady states, and obtaining link traffic carrying distribution data for each source node includes:

[0017] Obtaining a response amplitude of each target node in each new steady state through a response matrix; the response matrix is ​​used to describe: the response amplitude between different pairs of nodes after the target network is disturbed in the steady state, and the response amplitude between different pairs of nodes after each link stops propagating;

[0018] Comparing the response amplitude of each target node in the new steady state and the two new steady states, and calculating the difference in the response amplitude of each target node;

[0019] Based on the difference in response amplitudes of each target node obtained after each source node applies disturbance, the traffic carried by each link during the information propagation process of different source nodes is determined, and the link traffic carrying distribution data of each source node is obtained.

[0020] In one embodiment, the response matrix is ​​an N*N matrix, where N represents the number of rows or columns; each element of the response matrix represents the response relationship of a node pair; each row corresponding to the source node in the response matrix represents the difference in response amplitude between the remaining nodes when reaching the new steady state and when being in the initial steady state after the disturbance is applied from the source node.

[0021] In one embodiment, determining the dominant path according to the link traffic carrying distribution data corresponding to each source node includes:

[0022] Determining the traffic carrying capacity of each link in the target network according to the link traffic carrying distribution data corresponding to each source node;

[0023] The dominant path is determined according to the traffic carrying capacity of each link.

[0024] In one embodiment, determining the dominant path according to the traffic carrying capacity of each link includes:

[0025] Sorting the traffic carrying capacity of each link to obtain a target sequence;

[0026] Performing a binary search operation according to the target sequence, and determining a target threshold value through the result of the binary search operation;

[0027] A plurality of target links are screened out according to the target threshold, and the dominant path is determined according to each of the target links; the dominant path can connect all pairs of source nodes and target nodes.

[0028] In a second aspect, an embodiment of the present invention further provides a dominant path identification system in a multi-source scenario based on a perturbation response model, the system comprising:

[0029] a data input module, configured to determine node indexes of multiple pairs of source nodes and target nodes in a target network, and input the graph structure data of the target network and the node indexes into a perturbation response model;

[0030] The perturbation response model is used to apply perturbations to each source node in turn for each link in the target network, respectively, when the propagation function of the link is normal and stopped, and to make the target network evolve from an initial steady state to a new steady state, thereby obtaining two new steady states; based on the response amplitude of each target node in the initial steady state and the two new steady states, determine the traffic carried by the link during the information propagation process of different source nodes, and obtain the link traffic carrying distribution data of each source node; and determine the dominant path based on the link traffic carrying distribution data corresponding to each source node.

[0031] In a third aspect, an embodiment of the present invention further provides a terminal comprising a memory and one or more processors; the memory stores one or more programs; the programs include instructions for executing any of the methods for identifying a dominant path in a multi-source scenario based on a perturbation response model as described above; and the processor is used to execute the programs.

[0032] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium on which a plurality of instructions are stored, wherein the instructions are suitable for being loaded and executed by a processor to implement the steps of the dominant path identification method in a multi-source scenario based on a perturbation response model as described above.

[0033] Beneficial effects of the present invention: The embodiment of the present invention dynamically analyzes the impact of link state changes on the steady state of the system based on the perturbation response method, rather than relying on static indicators, which is closer to the real interaction mechanism of the network, making the mechanism modeling more accurate. By traversing the link state, the actual contribution of each link to the target node response is quantified, avoiding ignoring the complex interactions of weak connections and multi-link collaboration. Through the process of the system re-evolving to the steady state after the disturbance, the traffic distribution and competition at the shared nodes are naturally modeled, rather than relying on local optimal addressing, which improves the global rationality of the dominant path identification and solves the problem of multi-source target resource competition. The method of the present invention is applicable to all types of networks and plays a core supporting role in constructing a communication risk warning model based on network science, 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 13 is a flow chart of a method for identifying a dominant path in a multi-source scenario based on a perturbation response model provided by an embodiment of the present invention.

[0036] Figure 2 3 is a module diagram of a dominant path identification system in a multi-source scenario based on a perturbation response model 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 dominant paths in a multi-source scenario based on a perturbation response model. 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 embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention.

[0039] Those skilled in the art will appreciate that, unless otherwise specified, the singular forms "a," "an," "said," and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the stated features, integers, steps, or operations, but does not preclude the presence or addition of one or more other features, integers, steps, or operations. It should be understood that the term "and / or" used herein includes all or any combination of one or more of the 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 dominant paths in a multi-source scenario based on a perturbation response model, the method comprising: determining node indexes of multiple pairs of source nodes and target nodes in a target network, inputting the graph structure data of the target network and the node indexes into a perturbation response model; determining dominant paths in a multi-node interaction scenario through the perturbation response model; the perturbation response model is used to: for each link in the target network, apply perturbations to each source node in turn under two link states: normal and stopped propagation of the link, and cause the target network to re-evolve from an initial steady state to a new steady state, obtaining two new steady states; based on the response amplitudes of each target node in the initial steady state and the two new steady states, determine the traffic carried by the link during the information propagation process of different source nodes, obtain link traffic carrying distribution data of each source node; and determine the dominant path based on the link traffic carrying distribution data corresponding to each source node. The present invention dynamically analyzes the impact of link state changes on system steady state based on perturbation response, rather than relying on static indicators, which is closer to the actual interaction mechanism of the network and makes mechanism modeling more accurate. And by traversing the link status, the actual contribution of each link to the target node response is quantified, avoiding ignoring the complex interactions of weak connections and multi-link collaboration. In addition, through the process of the system re-evolving to a steady state after the disturbance, the traffic distribution and competition at the shared node are naturally modeled, rather than relying on local optimal addressing, thereby improving the global rationality of dominant path identification and solving the problem of multi-source target resource competition. The method of the present invention can be applied to various types of networks in reality, and in particular, it provides a new solution for the dominant path search process in multi-source-target scenarios. It 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 node indexes of multiple pairs of source nodes and target nodes in a target network, and inputting graph structure data of the target network and the node indexes into a perturbation response model.

[0044] Specifically, the target network in this embodiment can be any network for which a dominant path needs to be identified. The graph structure data of the target network and the node indexes of the source and target nodes that need to exchange information are obtained, and the graph structure data and node indexes are input into a pre-established perturbation response model.

[0045] Step S200: determining a dominant path in a multi-node interaction scenario using the perturbation response model;

[0046] The perturbation response model is used to: for each link in the target network, apply perturbations to each source node in turn under the two link states of normal and stopped propagation of the link, and make the target network evolve from an initial steady state to a new steady state, thereby obtaining two new steady states; determine the traffic carried by the link during the information propagation process of different source nodes based on the response amplitude of each target node in the initial steady state and the two new steady states, and obtain the link traffic carrying distribution data of each source node; determine the dominant path based on the link traffic carrying distribution data corresponding to each source node.

[0047] In summary, this embodiment redefines the way information flow permeates, perturbing the target network, which is originally in a steady state, through a perturbation response model. Perturbations disrupt the original steady-state equilibrium, and after re-evolving to a steady state, the dominant path of information flow propagation is determined. Perturbation analysis can track the propagation of information between interacting particles. The stability of a nonlinear system is evaluated by observing its response to local perturbations, which can gradually converge to the original stable point, diverge instability, or indicate cyclic or chaotic behavior. During the perturbation and steady-state evolution process, a perturbation is applied to a source node for each link in both normal propagation and stopped propagation states, causing the network to evolve from the initial steady state to a new steady state, and obtaining the steady-state responses in both states. By comparing the difference in the response amplitude of the target node in the two steady states, the traffic carrying capacity of each link corresponding to the source node is determined, and the link traffic carrying distribution data is obtained. The link traffic carrying distribution data can reflect the actual contribution of each link to the information transmission between the source node and the corresponding target node. Finally, the link traffic distribution data for all source nodes is integrated, and by analyzing the traffic distribution under multi-source interaction, the dominant paths carrying critical traffic are screened. This embodiment uses a dynamic analysis method that combines link state control with disturbance response observation to quantify the actual role of each link in multi-source transmission, avoiding reliance on static indicators or local optimal assumptions, thereby accurately capturing the dominant paths of multi-node interaction in complex networks.

[0048] For example, the target network contains multiple nodes, and the link between two nodes is a link or edge. For a network in a steady state, identify multiple pairs of source and target nodes involved in information exchange. Starting from the initial steady state, introduce a slight perturbation to each source node. Information propagation is stopped on each link one at a time, and the network then re-evolves to a new steady state. For each node, the magnitude of the node's response to the perturbation is calculated by subtracting the response amplitude at each new steady state from the initial steady state. By comparing the response amplitudes of all target nodes before and after the link's information propagation is stopped, the traffic distribution across all links for a specific source node transmitting to all target nodes is determined, thus obtaining the link traffic distribution data for that specific source node. Link traffic distribution data can be expressed in vector form, meaning each source node corresponds to a vector describing the traffic carried on the link. Ultimately, the overall link traffic distribution data for the multi-source-target scenario is obtained. Subsequently, appropriate thresholds can be set to further identify dominant paths.

[0049] In one implementation, a disturbance is applied to each source node in sequence under two link states, namely, normal and stopped propagation of the link, and the target network is caused to re-evolve from an initial steady state to a new steady state, thereby obtaining two new steady states, including:

[0050] In each link state, applying a disturbance of a specific amplitude to one of the source nodes of the target network in an initial steady state, so that the target network enters an unsteady state;

[0051] By solving the ordinary differential equation corresponding to the information flow dynamics equation, the target network in the non-steady state is re-evolved to a new steady state; the information flow dynamics equation is used to describe the node interaction behavior in different scenarios.

[0052] In general, perturbation response models include information flow dynamics equations, which are used to describe node interaction behavior in different scenarios. These equations can adapt to the diverse scenarios of information propagation between multiple pairs of source and target nodes. These equations consist of two data terms: one considers how a node evolves after receiving information, and the other describes how the node is affected by the information source. By selecting appropriate nonlinear terms, different information reception behaviors can be described.

[0053] Specifically, when the target network is in its initial steady state, a disturbance of a specific magnitude (e.g., injecting a specific amount of traffic) is applied to a source node, disrupting the system's equilibrium and causing it to enter an unsteady state. The dynamic interaction rules between nodes in the network are characterized using information flow dynamics equations. Solving this set of equations simulates the network's gradual evolution from the initial state after the disturbance to a new steady state under unsteady conditions. This process is repeated for each link, both under normal link transmission and under non-transmission conditions. By comparing the differences in the target node's responses in the new steady state under these two conditions, the link's actual contribution to information transmission between the source and target nodes (e.g., traffic carrying capacity) can be quantified.

[0054] In other words, by quantifying the carrying capacity of a path during information propagation, the dominant path can be determined. However, given the source and destination nodes, calculating the carrying capacity of all paths is too complex. Therefore, this embodiment converts the quantified carrying capacity of the path into the quantified traffic carrying capacity of each link, thereby only requiring traversal of all links in the target network. Specifically, quantifying the traffic carrying capacity of a link can be achieved by stopping transmission activity on the link during information propagation. Its essence is to transform the implicit role of a link (such as traffic carrying capacity) into a measurable steady-state response difference through perturbation, evolution, and observation.

[0055] It should be noted that stopping the transmission activity of a link only restricts the functionality of a certain edge or link in the target network. It does not delete the link from the target network, nor does it restrict the activities of the two nodes connected by the link. The two nodes can still exchange information with other nodes through other links.

[0056] For example, consider a nodes and Linked network .definition An independent information source, namely the source node ; An independent receiving target, namely the target node .

[0057] The information flow and its penetration behavior are represented by the following dynamic equation, which is an ordinary differential equation:

[0058] ;

[0059] in, 、 Represents nodes respectively 、 The value of opinion or information at a certain moment in time; Representation node exist The value changed within the change step time; Indicates the change step size, indicating a very short time. The first data item , used to reflect how the node itself evolves after receiving information; the second data item , which is used to reflect how the node itself is affected by the information source, and can describe different information reception behaviors by selecting appropriate nonlinear terms; Indicates when and Nonlinear mapping behavior for information interaction; express and When interacting with information, actively respond to the received The nonlinear mapping behavior of information interaction can be regarded as the transformation of its own information and node The information is mapped into a coupling space; Represents a slave node Information is passed to the node The rate can be unified as the elements of the adjacency matrix, or other weights can be used instead.

[0060] In actual application scenarios, the dynamic equations can be changed according to different 、 as well as , and then simulate the node interaction patterns in different scenarios, find the information flow propagation patterns in different scenarios, and thus find the dominant paths in different patterns through evolution.

[0061] According to the dynamic equation, when the system reaches a steady state, the derivative is 0, as shown in the following equation:

[0062] ;

[0063] The steady-state solution of the dynamic equation is:

[0064] ;

[0065] in, yes The result of negating the function is, represents an intermediate variable; Representation node The in-degree of a node is determined by its weighted degree and dynamic equation.

[0066] The subsequent experimental results are obtained based on the following information flow dynamics equation, which is similar to the above dynamic equation, but the information flow dynamics equation is set according to the specific application scenario. 、 as well as :

[0067] ;

[0068] in, express Time node The opinion value or information level; express Time node The opinion value or the square of the information level.

[0069] In one implementation, solving the ordinary differential equation corresponding to the information flow dynamics equation includes:

[0070] The ordinary differential equation corresponding to the information flow dynamics equation is solved by the Runge-Kutta algorithm.

[0071] Specifically, in a steady state, the ordinary differential equation evaluates to zero. When a disturbance is introduced to the source node, the steady state of the entire system is disrupted, and the ordinary differential equation no longer evaluates to zero. The Runge-Kutta algorithm is a numerical method for solving ordinary differential equations that improves the accuracy of approximate solutions through multi-stage slope estimation. This embodiment uses the Runge-Kutta algorithm to re-evolve the unstable system back to a steady state.

[0072] In one implementation, determining the traffic carried by the link during information propagation at different source nodes based on the response amplitude of each target node in the initial steady state and the two new steady states, and obtaining link traffic carrying distribution data for each source node includes:

[0073] Obtaining a response amplitude of each target node in each new steady state through a response matrix; the response matrix is ​​used to describe: the response amplitude between different pairs of nodes after the target network is disturbed in the steady state, and the response amplitude between different pairs of nodes after each link stops propagating;

[0074] Comparing the response amplitude of each target node in the new steady state and the two new steady states, and calculating the difference in the response amplitude of each target node;

[0075] Based on the difference in response amplitudes of each target node obtained after each source node applies disturbance, the traffic carried by each link during the information propagation process of different source nodes is determined, and the link traffic carrying distribution data of each source node is obtained.

[0076] In general, a perturbation response model also includes a response matrix. This embodiment introduces a response matrix to describe the magnitude of a node's response to a perturbation. The response matrix can be used not only to describe the magnitude of the response between different node pairs in a steady-state network under perturbation, but also to describe the magnitude of the response between different node pairs after a link stops propagating.

[0077] Specifically, perturbations are applied to the source nodes one by one. After the perturbations reach a steady state through evolution using the Runge-Kutta method, the viewpoint values ​​of each node are in a new steady state. At this point, the response matrix is ​​calculated. The size of the response matrix is: number of nodes * number of nodes. The elements of each row of the response matrix reflect the response amplitude or change magnitude of other nodes when the node pointed to by the row index is used as the source node. In other words, the response matrix is ​​used to store response amplitudes. The elements in the response matrix are essentially a quantification of the impact of the perturbation propagation between nodes, reflecting the direct or indirect impact of the link on the information transmission between the source and target nodes, whether in a normal transmission state or a stopped transmission state.

[0078] In practical applications, taking a link as an example, a perturbation is applied to a source node in both normal and stopped link states, causing the target network to evolve from its initial steady state to a new steady state. For each target node, the response amplitudes in the two new steady states are obtained using the response matrix. The response amplitudes in the initial steady state are then obtained, and the following formula is used to calculate: (response amplitude of the first new steady state - response amplitude of the initial steady state) - (response amplitude of the second new steady state - response amplitude of the initial steady state) = final response amplitude difference for the target node. The response amplitude differences for the corresponding target node indexes are summed to obtain the traffic carried by the link during information propagation from the source node; or, for the source node, the traffic carrying capacity of the link to all target nodes. In other words, the traffic carrying capacity of each link is related to the total traffic actually carried by the link in transmission from the source node to all target nodes.

[0079] By determining the traffic carried by each link during information propagation from different source nodes, we can obtain link traffic distribution data for each source node. Link traffic distribution data can be a vector, where each element corresponds to a link and its value is the traffic carried by that link during information propagation from that source node, or the traffic carrying capacity of that link for all destination nodes for that source node.

[0080] This embodiment uses a response matrix to construct a quantitative relationship between link status and steady-state response. By leveraging the difference in response amplitude between normal and stopped transmission states, the contribution of a single link to the propagation of disturbances to multiple target nodes is converted into a measurable traffic carrying value. This provides a quantitative basis at the mechanism level for subsequent dominant path identification.

[0081] For example, we can track how a local disturbance in a node's steady-state activity affects the information of all remaining nodes in the system, and find the degree of influence of the source node on other nodes in the information propagation. To describe the global response amplitude driven by disturbance, The specific element form is as follows:

[0082] ;

[0083] in, express The differential form of express The differential form of represents a logarithmic function with a natural constant as the base; Representation node The percentage change in node The percentage change of the node can be captured To Node The ratio of the change before and after the information is propagated. This dimensionless method of using the change divided by the original quantity makes the response coefficient applicable to node activities of different magnitudes (e.g. and may differ by several orders of magnitude), making it applicable to a variety of scenarios.

[0084] In the target network, give the node The total amount of response disturbance can be obtained by summing the absolute values ​​of the steady-state changes of other nodes caused by the disturbance. Global response :

[0085] ;

[0086] For example, in a steady-state network, each node has its own information level, represented by a vector called steady_state_old. In the steady-state state, the ordinary differential equation evaluates to zero. When a perturbation is introduced to a source node, the steady state of the entire system is disrupted, and the differential equation no longer evaluates to zero. After re-iterating to the steady state using the Runge-Kutta algorithm, the information level of the entire network changes. Assume that in the new steady state, the node's steady-state vector is steady_state_new1. Then, stop propagation on a link and re-evolve the system from steady_state_old (adding the same perturbation to the original source node) until the system reaches a new steady state. Assume that when the new steady state is reached, the node's steady-state vector is steady_state_new2.

[0087] The way to quantify the role of this link in the information transmission process is:

[0088] sum(abs(steady_state_new1-steady_state_new2));

[0089] Among them, sum represents the sum operation, and abs represents the absolute value operation.

[0090] In one implementation, the response matrix is ​​an N*N matrix, where N represents the number of rows or columns; each element of the response matrix represents the response relationship of a node pair; each row corresponding to the source node in the response matrix represents the difference in response amplitude between the remaining nodes when they reach the new steady state and when they are in the initial steady state after the disturbance is applied from the source node.

[0091] Specifically, the response matrix is ​​the sole source for calculating a link's traffic carrying capacity. The response matrix is ​​an N*N matrix, with each row representing the absolute value of the difference between the new steady-state reached by other nodes and the old steady-state after the perturbation is introduced from the source node. In a multi-source-target scenario, only the rows of the matrix corresponding to the source nodes need to be analyzed.

[0092] For example, in a multi-source-target scenario, the traffic carrying capacity of a link can be quantified without summing up the entire rows of the response matrix. Instead, it is necessary to first determine the rows in the response matrix corresponding to the source node indices, and then determine the elements in the columns corresponding to the target node indices in these rows and sum them up.

[0093] Link For example, to quantify its traffic carrying capacity during information exchange, the network can be evolved to an initial steady state, assuming the initial steady state is old_steady. Then, a disturbance is applied to a source node, and the network is allowed to evolve to a new steady state new_steady; then the link is stopped. The transmission activity of old_steady is added with the same disturbance again, and a new steady state new_steady_without_ .

[0094] Comparing new_steady and new_steady_without_ The differences in the target node in are calculated and these differences are accumulated. Thus, the traffic carrying capacity of the link calculated based on the response matrix is ​​expressed as follows:

[0095] ;

[0096] in, Indicates a link traffic carrying capacity.

[0097] However, to calculate the traffic carrying capacity of all links, the evolution process takes a long time. For example, if the target network has E links, it means that E evolutions are required.

[0098] This embodiment shortens the time taken for the system to evolve into a steady state by using a formula. First, the calculation formula for the response matrix is:

[0099] ;

[0100] in, Indicates that the node Indirectly affects nodes The response amplitude of needs to be solved by the chain rule; Indicates a link In the transfer slave node the magnitude of the flow carried by the incoming disturbance;

[0101] ;

[0102] in, Indicates solution How to calculate ? express The derivative of express The derivative of Indicates that Substitution The value of the function; Representation node The information level value of the intermediate node in the propagation process.

[0103] The traffic carrying capacity of a specific link during information dissemination can be directly calculated using the following formula:

[0104] ;

[0105] in, is the source node; is the target node; is the number of source nodes; is the number of target nodes; Indicates that the node Affects the node The response amplitude of Indicates that the node Affects nodes Through this formula, only two evolutions are needed to calculate the traffic carrying capacity of each link, so that the method of this embodiment can be applied to large-scale networks.

[0106] In one implementation, determining the dominant path according to the link traffic carrying distribution data corresponding to each source node includes:

[0107] Determining the traffic carrying capacity of each link in the target network according to the link traffic carrying distribution data corresponding to each source node;

[0108] The dominant path is determined according to the traffic carrying capacity of each link.

[0109] Specifically, by applying a specific perturbation to each source node, we can obtain a vector describing the link traffic load at that source node, i.e., the link traffic load distribution data. Therefore, each source node corresponds to a vector describing the link traffic load. Averaging the vectors of all source nodes yields the link traffic load distribution in a multi-source-destination scenario. Finally, based on the link traffic load distribution in this multi-source-destination scenario and a reasonable threshold, we can identify the dominant path.

[0110] It's important to note that perturbations are permanent. Most relevant systems reside near a permanent critical point and, in the long run, are insensitive to transient perturbations. These perturbations decay over time and rarely penetrate deeply into the network. Therefore, the amount of information passing through a node depends more on the stability of the system than on the dynamic characteristics of that node.

[0111] In one implementation, determining the dominant path according to the traffic carrying capacity of each link includes:

[0112] Sorting the traffic carrying capacity of each link to obtain a target sequence;

[0113] Performing a binary search operation according to the target sequence, and determining a target threshold value through the result of the binary search operation;

[0114] A plurality of target links are screened out according to the target threshold, and the dominant path is determined according to each of the target links; the dominant path can connect all pairs of source nodes and target nodes.

[0115] In summary, each link's traffic carrying capacity is calculated using a perturbation response model. Traffic carrying capacity is a quantitative measure of its actual contribution to information transmission in a multi-source-target scenario. Higher values ​​indicate a link carries more traffic and plays a more critical role in perturbation propagation. All links are sorted from high to low by traffic carrying capacity to form a sequence. A binary search operation quickly locates a reasonable threshold within the traffic carrying capacity sequence, effectively obtaining the target threshold. This ensures that links above this threshold cover the primary perturbation propagation links while avoiding excessive inclusion of secondary links.

[0116] Specifically, the target threshold is designed to ensure that, during information propagation, the network reconstructed by links with a traffic carrying capacity greater than the threshold can connect all source and destination nodes. In other words, the target threshold must ensure that the network reconstructed by target links selected based on this threshold can connect all pairs of source and destination nodes. After sorting by traffic carrying capacity, a binary search is then used to gradually test the network to find a reasonable threshold, which is the target threshold. If desired, the retention ratio of target links can be adjusted to achieve this.

[0117] In practical applications, links with traffic carrying capacity greater than the target threshold are initially identified as links included in the dominant path, i.e., target links. A network is reconstructed based on each target link, and links corresponding to nodes with a degree of 1 are deleted to ensure that no nodes with a degree of 1 exist in the network (except for the source and target nodes). Node degree is a measure of how many neighbors a node has. Links corresponding to nodes with a degree of 1 need to be deleted because traffic must flow both in and out, so links corresponding to nodes with a degree of 1 cannot be dominant links.

[0118] If the network reconstructed using the target links selected based on the target threshold can connect all source and target nodes, the threshold is increased, the proportion of target links is reduced, and connectivity is tested again. If the network reconstructed using the target links selected based on the target threshold cannot connect all source and target nodes, the threshold is decreased, the proportion of target links is increased, and connectivity is tested again. The optimal target threshold is to find a threshold that just connects all source and target nodes. This threshold can be determined based on the return value of the binary search algorithm. The binary search algorithm finds a sequence index that represents the optimal result. Therefore, the sequence obtained by sorting the traffic carrying capacity of each link is input into the binary search algorithm, and the element value corresponding to the sequence index returned by the binary search algorithm is used as the final target threshold.

[0119] Based on the above embodiments, the present invention also provides a dominant path identification system in a multi-source scenario based on a perturbation response model, such as Figure 2 As shown, the system includes:

[0120] A data input module 01 is configured to determine node indexes of multiple pairs of source nodes and target nodes in a target network, and input the graph structure data of the target network and the node indexes into a perturbation response model;

[0121] The perturbation response model 02 is used to apply perturbations to each source node in turn for each link in the target network, respectively, when the propagation function of the link is normal and stopped, and to make the target network evolve from an initial steady state to a new steady state, thereby obtaining two new steady states; based on the response amplitude of each target node in the initial steady state and the two new steady states, the traffic carried by the link during the information propagation process of different source nodes is determined, thereby obtaining the link traffic carrying distribution data of each source node; based on the link traffic carrying distribution data corresponding to each source node, a dominant path is determined.

[0122] 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 multi-source scenario based on a perturbation response model is implemented. The display screen of the terminal can be a liquid crystal display or an electronic ink display.

[0123] 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.

[0124] 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 multi-source scenario based on a perturbation response model.

[0125] 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).

[0126] In summary, the present invention discloses a method and system for identifying a dominant path in a multi-source scenario based on a perturbation response model, which relates to the field of computer technology. The method includes: determining the node indexes of multiple pairs of source nodes and target nodes in a target network, inputting the graph structure data of the target network and the node indexes into a perturbation response model; determining the dominant path in a multi-node interaction scenario through the perturbation response model; the perturbation response model is used to: for each link in the target network, apply perturbations to each source node in turn under the two link states of normal and stopped propagation of the link, and make the target network evolve from an initial steady state to a new steady state, thereby obtaining two new steady states; according to the response amplitude of each target node in the initial steady state and the two new steady states, determine the traffic carried by the link during the information propagation process of different source nodes, and obtain the link traffic carrying distribution data of each source node; according to the link traffic carrying distribution data corresponding to each source node, determine the dominant path. The present invention dynamically analyzes the impact of link state changes on the steady state of the system based on the perturbation response method, rather than relying on static indicators, which is closer to the real interaction mechanism of the network and makes the mechanism modeling more accurate. And by traversing the link state, the actual contribution of each link to the target node response is quantified, avoiding ignoring the complex interaction of weak connections and multi-link collaboration. In addition, through the process of the system re-evolving to the steady state after the disturbance, the traffic distribution and competition at the shared node are naturally modeled, rather than relying on local optimal addressing, thereby improving the global rationality of the dominant path identification and solving the problem of multi-source target resource competition. The method of the present invention can be applied to various types of networks in reality, especially providing a new solution for the dominant path search process in multi-source-target scenarios. It plays a core supporting role in constructing a communication risk warning model based on network science, implementing key node intervention strategies, and optimizing the intelligent governance mechanism of social information systems.

[0127] 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 dominant paths in multi-source scenarios based on a perturbation response model, characterized in that: The method comprises: Determining node indexes of multiple pairs of source nodes and target nodes in a target network, and inputting graph structure data of the target network and the node indexes into a perturbation response model; Determining the dominant path in a multi-node interaction scenario using the perturbation response model; The perturbation response model is used to: for each link in the target network, apply perturbations to each source node in sequence when the link's propagation function is normal or stopped, and cause the target network to re-evolve from an initial steady state to a new steady state, thereby obtaining two new steady states; determine the traffic carried by the link during information propagation at different source nodes based on the response amplitudes of the target nodes in the initial steady state and the two new steady states, thereby obtaining link traffic carrying distribution data for each source node; and determine the dominant path based on the link traffic carrying distribution data corresponding to each source node. Applying disturbances to each of the source nodes in sequence under the two link states of normal and stopped propagation of the link, respectively, and causing the target network to re-evolve from the initial steady state to a new steady state, thereby obtaining two new steady states, including: In each link state, applying a disturbance of a specific amplitude to one of the source nodes of the target network in an initial steady state, so that the target network enters an unsteady state; By solving the ordinary differential equation corresponding to the information flow dynamics equation, the target network in the non-steady state is re-evolved to a new steady state; the information flow dynamics equation is used to describe the node interaction behavior in different scenarios.

2. The method for identifying dominant paths in a multi-source scenario based on a perturbation response model according to claim 1, characterized in that: By solving the ordinary differential equations corresponding to the information flow dynamics equation, including: The ordinary differential equation corresponding to the information flow dynamics equation is solved by the Runge-Kutta algorithm.

3. The method for identifying dominant paths in a multi-source scenario based on a perturbation response model according to claim 1, characterized in that: Determining, based on the response amplitude of each of the target nodes in the initial steady state and the two new steady states, the traffic carried by the link during the information propagation process of different source nodes, and obtaining link traffic carrying distribution data for each source node, including: Obtaining a response amplitude of each target node in each new steady state through a response matrix; the response matrix is ​​used to describe: the response amplitude between different pairs of nodes after the target network is disturbed in the steady state, and the response amplitude between different pairs of nodes after each link stops propagating; Comparing the response amplitude of each of the target nodes in the initial steady state and the two new steady states, and calculating the difference in the response amplitude of each of the target nodes; Based on the difference in response amplitudes of each target node obtained after each source node applies disturbance, the traffic carried by each link during the information propagation process of different source nodes is determined, and the link traffic carrying distribution data of each source node is obtained.

4. The method for identifying dominant paths in a multi-source scenario based on a perturbation response model according to claim 3, characterized in that: The response matrix is ​​an N*N matrix, where N represents the number of rows or columns; each element of the response matrix represents the response relationship of a node pair; each row corresponding to the source node in the response matrix represents the difference in response amplitude between the remaining nodes when they reach the new steady state and when they are in the initial steady state after the disturbance is applied from the source node.

5. The method for identifying dominant paths in a multi-source scenario based on a perturbation response model according to claim 1, characterized in that: Determining the dominant path according to the link traffic carrying distribution data corresponding to each source node includes: Determining the traffic carrying capacity of each link in the target network according to the link traffic carrying distribution data corresponding to each source node; The dominant path is determined according to the traffic carrying capacity of each link.

6. The method for identifying dominant paths in a multi-source scenario based on a perturbation response model according to claim 5, characterized in that: Determining the dominant path according to the traffic carrying capacity of each link includes: Sorting the traffic carrying capacity of each link to obtain a target sequence; Performing a binary search operation according to the target sequence, and determining a target threshold value through the result of the binary search operation; A plurality of target links are screened out according to the target threshold, and the dominant path is determined according to each of the target links; the dominant path can connect all pairs of source nodes and target nodes.

7. A dominant path identification system in a multi-source scenario based on a perturbation response model, characterized in that: The system comprises: a data input module, configured to determine node indexes of multiple pairs of source nodes and target nodes in a target network, and input the graph structure data of the target network and the node indexes into a perturbation response model; The perturbation response model is used to apply perturbations to each source node in sequence for each link in the target network, respectively, when the link's propagation function is normal and stopped, and to cause the target network to re-evolve from an initial steady state to a new steady state, thereby obtaining two new steady states; based on the response amplitudes of each target node in the initial steady state and the two new steady states, determine the traffic carried by the link during the information propagation process of different source nodes, thereby obtaining link traffic carrying distribution data for each source node; and based on the link traffic carrying distribution data corresponding to each source node, determine a dominant path; Applying disturbances to each of the source nodes in sequence under the two link states of normal and stopped propagation of the link, respectively, and causing the target network to re-evolve from the initial steady state to a new steady state, thereby obtaining two new steady states, including: In each link state, applying a disturbance of a specific amplitude to one of the source nodes of the target network in an initial steady state, so that the target network enters an unsteady state; By solving the ordinary differential equation corresponding to the information flow dynamics equation, the target network in the non-steady state is re-evolved to a new steady state; the information flow dynamics equation is used to describe the node interaction behavior in different scenarios.

8. 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 multi-source scenario based on a perturbation response model as described in any one of claims 1-6; and the processor is used to execute the program.

9. 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 multi-source scenario based on a perturbation response model as described in any one of claims 1 to 6.

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