A network propagation simulation method and device introducing imitator nodes

Abstract

A network propagation simulation method and device that introduces an imitator node, wherein the imitator node becomes an active node after a designated node in its corresponding neighboring node becomes an active node, and the method includes: firstly constructing a random network graph; secondly determining the node type and state in the random network; thirdly determining the state update rules of the imitator node and the ordinary node; fourthly, cascading network information propagation; fifthly counting the number of active nodes after the number of active nodes in the random network is stable. The present invention expands the original threshold model, takes into account the role of the imitator node in the social network, and facilitates the study of how the heterogeneity of the behavior of the nodes allows information to propagate in the network.

Description

Technical Field

[0001] The present invention relates to the field of social information dissemination, and in particular to a network dissemination simulation method and device that introduces imitator nodes. Background Art

[0002] The threshold model is used to study the phenomenon of social contagion and is a common method used by researchers to study information propagation in networks. The threshold model has been well studied as a classic paradigm for studying the process of information propagation. The main focus is on how the underlying network structure or the size of the initial seed affects the cascade dynamics, and the strategy for each node to update its state in the proposed threshold model is the same, so in previous threshold models, the impact of heterogeneity in node behavior has been largely ignored. In fact, in real life, there are not only a variety of individuals in a network, but they generally exhibit different behaviors. Therefore, the current threshold model is single-minded in considering the impact of node behavior on information propagation in the network.

[0003] It can be seen that studying how the heterogeneity of node behavior affects the propagation of information in the network is an urgent problem to be solved by technical personnel in this field. Summary of the invention

[0004] The present invention aims to overcome the above-mentioned shortcomings of the prior art and provides a network propagation simulation method and device that introduces imitator nodes, so as to facilitate the study of how the heterogeneous behavior of nodes allows information to propagate in the network.

[0005] In order to solve the above technical problems, the present application provides a network propagation simulation method introducing an imitator node, wherein the nodes in the random network include ordinary nodes and imitator nodes; the method comprises:

[0006] S1. Construct a random network: The network set is G = (V, E), and its node set and edge set are V = {v1, v2, ..., v n}and The total number of nodes N;

[0007] S2. Determine the node type and state in the random network. There are two types of nodes in the random network: ordinary nodes and imitator nodes; nodes have two states: active and inactive;

[0008] S3, proposing two strategies for the imitator node to follow the designated node: a random following strategy and a fixed following strategy, and setting status update rules for the two nodes;

[0009] S4, in the network propagation in which the imitator node is introduced, after the number of active nodes in the random network is stabilized, counting the number of active nodes;

[0010] S5. Calculate the final scale of information dissemination.

[0011] Further, the steps of constructing a random network in step S1 are as follows:

[0012] S1.1. The random network is constructed according to the average network degree: by formula E all =N·z / 2 determines the total number of edges in the network, where E all is the total number of edges in the network, N is the total number of nodes in the network, and z is the average degree of the network.

[0013] Further, the steps of determining the node type and state in the random network described in step S2 are as follows:

[0014] S2.1, randomly select a proportion p of nodes in the network as imitator nodes, and the rest of the nodes are ordinary nodes;

[0015] S2.2. Initially, a portion of nodes (including ordinary nodes or imitator nodes) are randomly selected in the network as active nodes, and the remaining nodes are inactive nodes.

[0016] Further, the random following strategy and the fixed following strategy described in step S3 and setting the status update rules of the two nodes include:

[0017] S3.1, the state of the imitator node following different neighbor nodes in each time step under the random following strategy;

[0018] S3.2, the state of the imitator node following the same neighbor node in each time step under the fixed following strategy;

[0019] S3.3. The imitator node updates its state according to the following strategy; the ordinary node updates its state according to the original threshold model.

[0020] Furthermore, the number of active nodes in the random network described in step S4 is stable, which means:

[0021] S4.1. When there are no more inactive nodes in the network that transition to active node status, the propagation stops.

[0022] Furthermore, the final scale of calculating information propagation in step S5 includes:

[0023] For each group of experimental indicators, multiple experiments are carried out, and after the calculation and propagation are completed, the ratio of the number of active nodes in the random network to the total number of nodes is determined, and the ratio corresponds to the propagation range of the network propagation of the introduced imitator node under the characteristic parameters.

[0024] Furthermore, the characteristic parameters include the proportion p of imitator nodes to the total nodes and the average network degree z of the random network. The range of information propagation in the network will be simulated under different characteristic parameters.

[0025] The present invention provides a network propagation simulation method that introduces an imitator node, wherein the nodes in the network include ordinary nodes and imitator nodes, and the nodes include an active state (1 state) and an inactive state (0 state). The active node is a node in an active state, and the imitator node becomes an active node after a designated node in its neighboring nodes becomes an active node; the designated node is a node randomly selected from the neighboring nodes of the imitator node; the ordinary node becomes an active node after the number of active nodes in the corresponding neighboring nodes exceeds a set threshold. This scheme expands the original threshold model and takes into account the role of the imitator node in the network. The imitator node is different from other ordinary nodes. The ordinary 0-state node will change its state according to the threshold condition, while the imitator node will follow one of its neighbors. This is because in actual scenarios, in addition to taking the comprehensive opinions of multiple neighbors, individuals will also have a greater degree of trust in certain neighbors. In this case, individuals are more inclined to directly adopt the decision of a trustworthy neighbor.

[0026] Based on the original threshold model, which determines whether a node meets the threshold condition and then chooses whether to update its own status, the present invention gives the node imitation awareness, that is, adds simple imitation behavior of the node. This makes the proposed model more consistent with the heterogeneity of people's behavior on real social networks. Simulation results show that the heterogeneity of node behavior has an impact on the scope of network information dissemination.

[0027] The second aspect of the present invention relates to a network propagation simulation device for introducing an imitator node, comprising a memory and one or more processors, wherein the memory stores executable code, and when the one or more processors execute the executable code, the network propagation simulation method for introducing an imitator node of the present invention is implemented.

[0028] A computer-readable storage medium of the present invention stores a program, and when the program is executed by a processor, a network propagation simulation method of the present invention that introduces an imitator node is implemented.

[0029] The advantages of the present invention are: the model proposed by the present invention can be used to simulate the propagation of information in the network. The structure of the model is that the imitator node and the ordinary node participate in the information propagation in the network through imitation behavior and threshold mechanism respectively, which is more in line with the characteristics of heterogeneity of node behavior in the real network, making the simulation result more realistic. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0031] Figure 1 A flowchart of a network propagation simulation method introducing an imitator node provided by an embodiment of the present invention;

[0032] Figure 2 A random network schematic diagram provided for an embodiment of the present invention;

[0033] Figures 3(a) and 3(b) are graphs showing the changing trend of the proportion of 1-state nodes in information propagation over time for different p values ​​in a random network provided by an embodiment of the present invention, wherein Figure 3(a) is a graph showing the changing trend of the proportion of 1-state nodes in information propagation over time t under the strategy of fixed following by the imitator node, and Figure 3(b) is a graph showing the changing trend of the proportion of 1-state nodes in information propagation over time t under the strategy of random following by the imitator node.

[0034] Figures 4(a) and 4(b) are graphs showing the changing trend of the proportion of 1-state nodes with p-values ​​in information propagation at different z-values ​​in a random network provided by an embodiment of the present invention, wherein Figure 4(a) is a graph showing the changing trend of the proportion of 1-state nodes with p-values ​​in information propagation under the strategy of fixed following by the imitator node, and Figure 4(b) is a graph showing the changing trend of the proportion of 1-state nodes with p-values ​​in information propagation under the strategy of random following by the imitator node. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] The core of the present invention is to provide a network propagation simulation method introducing an imitator node, so as to make the model describing network information propagation more reasonable and improve the accuracy of the threshold model for determining the information propagation capability.

[0037] In order to enable those skilled in the art to better understand the present application, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0038] Example 1

[0039] In order to overcome the singleness of role behavior in existing threshold model research, based on the threshold model, combined with the diversity of roles in the network and the different performances of roles, the present invention proposes a network propagation simulation method that introduces imitator nodes: it proposes the influence of two following strategies of imitator nodes in the network on the final scale of information propagation, which has a great reference role for researchers to control network information propagation.

[0040] In order to solve the above technical problems, an embodiment of the present application provides a network propagation simulation method introducing an imitator node, wherein the nodes in the network include ordinary nodes and imitator nodes. Figure 1 A flowchart of a network propagation simulation method for introducing an imitator node provided in this embodiment; Figure 1 As shown, the method includes:

[0041] S1. Construct a random network: The network set is G = (V, E), and its node set and edge set are V = {v1, v2, …, v n}and The total number of nodes N;

[0042] S2. Determine the node type and state in the random network. There are two types of nodes in the random network: ordinary nodes and imitator nodes; nodes have two states: active and inactive;

[0043] S3, proposing two strategies for the imitator node to follow the designated node: a random following strategy and a fixed following strategy, and setting status update rules for the two nodes;

[0044] S4, in the network propagation in which the imitator node is introduced, after the number of active nodes in the random network is stabilized, counting the number of active nodes;

[0045] S5. Calculate the final scale of information dissemination.

[0046] The construction of the random network described in step S1 is specifically performed as follows:

[0047] The random network is constructed according to the average network degree: all =N·z / 2 determines the total number of edges in the network, where E all is the total number of edges in the network, N is the total number of nodes in the network, and z is the average degree of the network.

[0048] The steps of determining the node type and state in the random network described in step S2 are as follows:

[0049] S2.1, randomly select a proportion p of nodes in the network as imitator nodes, and the rest of the nodes are ordinary nodes;

[0050] S2.2. Initially, a portion of nodes (including ordinary nodes or imitator nodes) are randomly selected in the network as active nodes, and the remaining nodes are inactive nodes.

[0051] The random following strategy and the fixed following strategy described in step S3 and setting the status update rules of the two nodes include:

[0052] S3.1, the state of the imitator node following different neighbor nodes in each time step under the random following strategy;

[0053] S3.2, the state of the imitator node following the same neighbor node in each time step under the fixed following strategy;

[0054] S3.3. The imitator node updates its state according to the following strategy; the ordinary node updates its state according to the original threshold model.

[0055] The number of active nodes in the random network described in step S4 is stable, which means:

[0056] S4.1. When there are no more inactive nodes in the network that transition to active node status, the propagation stops.

[0057] The final scale of calculating information propagation described in step S5 includes:

[0058] For each group of experimental indicators, multiple experiments are carried out, and after the calculation and propagation are completed, the ratio of the number of active nodes in the random network to the total number of nodes is determined, and the ratio corresponds to the propagation range of the network propagation of the introduced imitator node under the characteristic parameters.

[0059] The characteristic parameters include the proportion p of imitator nodes to the total nodes and the average network degree z of the random network. The range of information propagation in the network will be simulated under different characteristic parameters.

[0060] The present invention provides a network propagation simulation method that introduces an imitator node, and the nodes in the random network include ordinary nodes and imitator nodes. Among them, the nodes include an active state (state 1) and an inactive state (state 0), and the active node is a node in an active state. After the designated node in the neighboring node corresponding to itself becomes an active node, the imitator node itself becomes an active node; the designated node is a node randomly selected from the neighboring node of the imitator node; after the number of active nodes in the corresponding neighboring node exceeds the set threshold, the ordinary node itself becomes an active node. This scheme expands the original threshold model and takes into account the role of the imitator node in the social network. The imitator node is different from other ordinary nodes. The ordinary 0-state node will change its state according to the threshold condition, while the imitator node will follow one of its neighbors. In actual scenarios, in addition to taking the comprehensive opinions of multiple neighbors, individuals will also have a greater degree of trust in certain neighbors. In this case, individuals are more inclined to directly adopt the decision of a trustworthy neighbor.

[0061] In some implementations, the imitator nodes include: imitator nodes under a random following strategy and imitator nodes under a fixed following strategy. Among them, the imitator nodes under the random following strategy follow the states of different neighbor nodes in each time step, and the imitator nodes under the fixed following strategy follow the states of the same neighbor node in each time step. This embodiment is based on the original threshold model and proposes a method for the imitator node to influence the propagation of information under the random following strategy and the fixed following strategy. These two strategies evolve the different effects of the imitator node on the propagation of information in social networks under different imitation strategies. Through numerical simulation, it can be found that the random following strategy can effectively promote the spread of information, while the fixed following strategy inhibits the spread of information. It provides a basis for controlling the spread of public opinion or other effective information on social networks.

[0062] In addition, the information dissemination capability of the extended threshold model may include information such as the speed and range of information dissemination. In the solution provided by the embodiment of the present invention, determining the information dissemination capability of the extended threshold model under the corresponding characteristic parameters according to the number of active nodes may specifically include: determining the information dissemination range of the extended threshold model under the corresponding characteristic parameters according to the number of active nodes. Specifically, the proportion of the number of active nodes to the total number of nodes may be determined, and the information dissemination range of the extended threshold model under the corresponding characteristic parameters may be determined according to the proportion.

[0063] The characteristic parameters include the proportion of imitator nodes to the total nodes, the average degree of the random network, and the average degree of the network is the average number of neighbor nodes of a node in the network.

[0064] The effects of the two following strategies based on the imitator node provided by the present invention are shown in Figures 3 and 4. Figure 3 is a diagram of a method provided by an embodiment of the present invention for different p values ​​in a random network, the proportion of 1-state nodes in information propagation (denoted by ρ in the figure) 1 (represents) a trend graph of changes over time. Figure 4 is a trend graph of the proportion of 1-state nodes in information propagation changing with p value at different z values ​​in a random network provided by an embodiment of the present application. As shown in Figure 3, Figure 3(a) and Figure 3(b) correspond to the fixed follow-up strategy of the imitator node and the random follow-up strategy of the imitator node, respectively. The final scale of the information cascade decreases with the increase of p value under the fixed follow-up strategy of the imitator node. The smaller the value of p, the wider the range of information propagation; on the contrary, the final scale of the information cascade does not seem to change much under the random follow-up strategy of the imitator node, because global cascades will occur. As shown in Figure 4, the different effects under the two different strategies can be more intuitively seen: Figure 4(a) Under the fixed follow-up strategy of the imitator node, as p increases, the final cascade ratio will become smaller and smaller; Figure 4(b) Under the random follow-up strategy of the imitator node, when z≤5, the final cascade ratio will decrease with the increase of p value, while when z≥6, the final cascade ratio z will increase with the increase of p value. The above experiments show that the two following strategies will lead to different effects on information propagation in random networks. The fixed following strategy can suppress information propagation, while the random following strategy can promote information propagation.

[0065] As described above, this application introduces a random network embodiment, and proposes a network propagation simulation method that introduces imitator nodes. By combining with simulation experiments, the two proposed imitator node following strategies can help researchers better understand the dual effects of imitator nodes on information propagation; combined with the original threshold model, the participation of imitator nodes in information transmission in the network is taken into account, and at the same time, it provides ideas for controlling the scale of various information propagation in real life. In particular, it has important reference significance for studying the evaluation information of a product. Through the method of the present invention, it can be considered how to arrange social robots with imitative behavior in social networks to achieve the role of recommending and resisting a product.

[0066] Example 2

[0067] The second aspect of the present invention relates to a network propagation simulation device that introduces an imitator node, including a memory and one or more processors, wherein the memory stores executable code, and when the one or more processors execute the executable code, they are used to implement a network propagation simulation method that introduces an imitator node in Example 1.

[0068] Example 3

[0069] A computer-readable storage medium of the present invention stores a program, and when the program is executed by a processor, a network propagation simulation method of introducing an imitator node in embodiment 1 is implemented.

[0070] The above is a detailed introduction to a network propagation simulation method for introducing an imitator node provided by the present invention. The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the various embodiments can be referenced to each other. It should be pointed out that for ordinary technicians in this technical field, without departing from the principle of the present invention, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

Claims

1. A network propagation simulation method introducing an imitator node, characterized in that: The nodes in the random network include ordinary nodes and imitator nodes, and the imitator node refers to a node that follows one of its neighbor nodes; the method includes: S1. Construct a random network: The network set is , whose node set and edge set are and , total number of nodes ; S2. Determine the node type and state in the random network. There are two types of nodes in the random network: ordinary nodes and imitator nodes. There are two states of nodes: active and inactive. S3, propose two strategies for the imitator node to follow the designated node: a random following strategy and a fixed following strategy and set status update rules for the two nodes; the designated node is a node randomly selected from the neighboring nodes of the imitator node; S4, in the network propagation in which the imitator node is introduced, after the number of active nodes in the random network is stabilized, counting the number of active nodes; S5. Calculate the final scale of information dissemination.

2. A network propagation simulation method introducing an imitator node according to claim 1, characterized in that: The specific steps of constructing the random network are as follows: The random network is constructed according to the average degree of the network: Determine the total number of edges in the network, where is the total number of edges in the network, is the total number of network nodes, is the average degree of the network.

3. A network propagation simulation method introducing an imitator node according to claim 1, characterized in that: The steps of determining the node type and state in the random network are as follows: S2.

1. Randomly select the proportion in the network The nodes are used as imitator nodes, and the rest of the nodes are common nodes; S2.

2. Initially, a portion of nodes (including ordinary nodes or imitator nodes) are randomly selected in the network as active nodes, and the remaining nodes are inactive nodes.

4. A network propagation simulation method introducing an imitator node according to claim 1, characterized in that: The random following strategy and the fixed following strategy and setting the status update rules of the two nodes include: S3.1, the state of the imitator node following different neighbor nodes in each time step under the random following strategy; S3.2, the state of the imitator node following the same neighbor node in each time step under the fixed following strategy; S3.

3. The imitator node updates its state according to the following strategy; the ordinary node updates its state according to the original threshold model.

5. A network propagation simulation method introducing an imitator node according to claim 1, characterized in that: The number of active nodes in the random network is stable when: When there are no more inactive nodes in the network that transition to active node status, the propagation stops.

6. A network propagation simulation method introducing an imitator node according to claim 1, characterized in that: The final scale of the computational information dissemination includes: For each group of experimental indicators, multiple experiments are carried out, and after the calculation and propagation are completed, the ratio of the number of active nodes in the random network to the total number of nodes is determined, and the ratio corresponds to the propagation range of the network propagation of the introduced imitator node under the characteristic parameters.

7. A network propagation simulation method introducing an imitator node according to claim 6, characterized in that: The characteristic parameters include the proportion of imitator nodes to total nodes and the average network degree of the random network , the scope of information propagation in the network will be simulated under different characteristic parameters.

8. A network communication simulation device introducing an imitator node, characterized in that: It comprises a memory and one or more processors, wherein the memory stores executable codes, and when the one or more processors execute the executable codes, they are used to implement a network propagation simulation method introducing an imitator node as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that: A program is stored thereon, and when the program is executed by a processor, a network propagation simulation method introducing an imitator node as described in any one of claims 1-7 is implemented.

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