A rumor tracing method based on infection depth aggregation

Abstract

The application discloses a rumor tracing method based on infection depth aggregation, and proposes the concepts of infection degree, infection depth and infection aggregation degree. First, based on the infection bottom map network before and after rumor propagation, the corresponding infection subgraph is obtained, and the infection degree of each node in the infection subgraph is calculated. Then, the nodes with the infection degree of 1 are added to the suspicious set of rumor source points by traversing the nodes of the infection subgraph. Next, the nodes in the suspicious set are searched by breadth-first search, and the infection depth of each node is calculated. Finally, the aggregation degree of the nodes in the suspicious set is calculated, the suspicious source point infection aggregation degree set is added, and the node with the maximum infection aggregation degree is selected as the presumed rumor source point in the suspicious source point infection aggregation degree set. The method can identify the rumor source point within a few steps, and the comprehensive tracing accuracy and average error distance are excellent and stable in different network topologies.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of computer network and information security technology, and particularly relates to a rumor tracing method based on infection depth aggregation. BACKGROUND

[0002] With the rapid development of the Internet, social media, online forums, instant messaging tools and other network platforms have become the main channel for information dissemination. However, these platforms also provide convenient conditions for the rapid spread of rumors, and rumor tracing in complex networks has always been a key issue of concern to scholars.

[0003] Current rumor tracing problems mainly rely on two main methods: one is based on infection centrality indicators, through infection subgraph, to construct source estimator, maximize rumor source detection probability, however, this method has high demand for complete transmission subgraph, and it is often difficult to obtain complete transmission path and infection node information in actual scenarios. The second is to set observers to collect node infection state and infection time information to detect rumor source points, and the tracing accuracy is limited by the location and number of observers, if the observers are not properly set or the number is insufficient, it may lead to the omission of key information, thereby affecting the accuracy of the tracing result.

[0004] With the expansion of the network size, the difficulty of data acquisition increases, and the computational complexity increases sharply, which seriously affects the efficiency of rumor tracing. Therefore, how to accurately and efficiently track the source of rumors between different topological networks has become an important problem to be solved. SUMMARY

[0005] The problem to be solved by the present application is to provide a rumor tracing method based on infection depth aggregation, to propose the concepts of node infection degree, infection depth and infection aggregation degree, to quickly identify rumor source points by selecting a suspicious set, and to improve the comprehensive tracing accuracy and average error distance in different network topologies.

[0006] The present application adopts the following technical scheme: a rumor tracing method based on infection depth aggregation, comprising the following steps:

[0007] S1, based on the infection bottom graph network before and after rumor propagation, obtaining the corresponding infection subgraph, and calculating the infection degree of each node in the infection subgraph;

[0008] S2, traversing the nodes of the infection subgraph, and adding the nodes with infection degree 1 to the suspicious set of rumor source points;

[0009] S3, traversing the suspicious set of rumor source points, performing a breadth-first search on the nodes in the suspicious set, and calculating the infection depth of each node;

[0010] S4, calculating the aggregation degree of the nodes in the suspicious set, and adding the suspicious source point infection aggregation degree set;

[0011] S5, in the suspicious source point infection aggregation degree set, select the node with the maximum infection aggregation degree as the presumed rumor source point.

[0012] Specifically, assuming that the infection bottom graph before and after rumor propagation is G(V, E), the infection subgraph is G i (V i ,E i ), any node The infection degree Inr(v) of the node v is calculated, and the formula is as follows:

[0013]

[0014] Wherein, V, V i respectively represent the set of nodes in the network before and after infection, E, E i respectively represent the set of edges in the network before and after infection, k v respectively represent the degree of node v in the infection subgraph and the infection bottom graph; if Inr(v) = 1, the node v is added to the suspicious set S of rumor source points.

[0015] Further, the nodes in the suspicious set S are traversed, for any node v ∈ S, breadth-first search BFS(G, v) is performed, the first node u is found, which satisfies Inr(u) < 1, and the infection depth from the node v is calculated, and the formula is as follows:

[0016] dep i (v) = d(v, u) - 1

[0017] Wherein, d(v, u) is the shortest path length from node v to node u.

[0018] Further, for any node v ∈ S in the suspicious set, the infection aggregation degree Cr(v) of the node v is calculated, and the formula is as follows:

[0019]

[0020] Wherein, g i l (v) represents the number of infection nodes with a step length of l from the node v; the step length l is set according to the shortest path length from the node v.

[0021] The infection aggregation degree of each node is calculated, added to the suspicious source point infection aggregation degree set C, and the node with the maximum infection aggregation degree in C As the presumed rumor source point.

[0022] The technical scheme of the present application also provides an electronic device, comprising:

[0023] One or more processors;

[0024] a storage device having stored thereon one or more programs;

[0025] The one or more programs, when executed by the one or more processors, cause the one or more processors to implement any of the rumor tracing methods based on infection depth aggregation described above.

[0026] The present application also provides a computer readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the steps of any of the rumor tracing methods based on infection depth aggregation described above.

[0027] Compared with the prior art, the present application has the following technical effects:

[0028] 1. The rumor tracing method based on infection depth aggregation of the present application proposes the concepts of infection degree, infection depth and infection aggregation degree, calculates the infection aggregation degree of nodes in the suspicious set by selecting the suspicious set, and selects the node with the largest infection aggregation degree as the rumor source point, so that the rumor source point can be quickly and effectively located.

[0029] 2. The rumor tracing method of the present application can identify the rumor source point within a few steps, and in different network topologies, the comprehensive tracing accuracy and average error distance are excellent and stable. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 is a flowchart of the rumor tracing method of the present application;

[0031] Figure 2 is an error distance diagram of different rumor tracing methods of the present application in four different networks;

[0032] Figure 3 is an average error distance diagram of different rumor tracing methods of the present application in different network topologies;

[0033] Figure 4 is an evolution process diagram of the average error distance of the rumor tracing method of the present application under different network topologies with the change of infection scale;

[0034] Figure 5 is an evolution process diagram of the average error distance of the rumor tracing method of the present application with the change of the average degree of the network;

[0035] Figure 6 is a change trend diagram of the average error distance in different network topologies under different infection rates. DETAILED DESCRIPTION

[0036] In order to make the purposes, technical solutions and advantages of the present application clearer, the technical solutions of the application will be further described in detail below with reference to the drawings. The described embodiments are only a part of the embodiments involved in the present application. All non-innovative embodiments of other researchers in the field on the basis of the embodiments belong to the protection scope of the present application. Meanwhile, the step numbers in the embodiments of the present application are only set for the convenience of description and explanation, and the order between the steps is not limited in any way. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0037] The rumor tracing method based on infection depth aggregation of the present application, as shown in the figure, comprises the following steps: Figure 1

[0038] S1, based on the infection base map network before and after rumor propagation, obtaining the corresponding infection subgraph, and calculating the infection degree of each node in the infection subgraph;

[0039] S2, traversing the nodes of the infection subgraph, and adding the nodes with an infection degree of 1 to the suspicious set of rumor source points;

[0040] S3, traversing the suspicious set of rumor source points, performing a breadth-first search on the nodes in the suspicious set, and calculating the infection depth of each node;

[0041] S4, calculating the aggregation degree of the nodes in the suspicious set, and adding the suspicious source point infection aggregation degree set;

[0042] S5, selecting the node with the maximum infection aggregation degree in the suspicious source point infection aggregation degree set as the presumed rumor source point.

[0043] In an embodiment of the present application, four different network base maps WS (Watts-Strogatz network), BA (Barabasi-Albert network), Facebook and LastFM network are used for experiments, wherein the Facebook and LastFM network come from the complex network data set of Stanford University. The infection rate is set to 0.8, the corresponding infection subgraph is obtained based on the SI propagation model, and the parameter settings of each base map network are shown in Table 1:

[0044]

[0045] Further, based on the parameter settings of each base map network in the above table, the rumor tracing is performed by the method of the present application.

[0046] Firstly, it is assumed that the base map network before and after rumor propagation and the obtained infection subgraph are G(V, E) and G i (V i ,E i ), respectively. ​

[0047] traversing the infected subgraph G i For any node v∈G i , record the degree of node v in the infected subgraph and the base graph network k v , calculate the infection degree of node v If Inr(v) = 1, that is, node v is full of infection, add v to the suspicious set S;

[0048] Then, traverse the nodes in S, for node v∈S, perform a breadth-first search BFS(G, v) to find the first node u that satisfies Inr(u) < 1.

[0049] Let the shortest path length from node v to node u be d(v, u), then the infection depth from node v is:

[0050] dep i (v) = d(v, u) - 1

[0051] Next, traverse the suspicious set S, for node v∈S, use to represent the number of infected nodes at a distance of l steps from node v, and the infection aggregation degree of node v is:

[0052]

[0053] Add the suspicious source node infection aggregation degree set C.

[0054] Finally, select the node with the largest infection aggregation degree in C as the rumor source point speculated by the method of this embodiment.

[0055] Further, the error distance of the rumor tracing method based on infection depth aggregation (IDC) of this embodiment and the existing rumor tracing algorithms based on closeness centrality (CC), based on betweenness centrality (BC) and reverse propagation algorithm (RP) in different base graph networks are compared, as shown in Figure 2 .

[0056] Figure 2 (a) in the figure is an error distance diagram in the artificial synthetic network WS, Figure 2 (b) in the figure is an error distance diagram in the artificial synthetic network BA, Figure 2 (c) in the figure is an error distance diagram in the real network Facebook, Figure 2 (d) in the figure is an error distance diagram in the real network LastFM.

[0057] It can be seen that the rumor tracing algorithm based on infection depth aggregation (IDC) performs well in four different network structure topologies, and can find the real source point within a limited step. Among them, the performance is best in the small world network WS, and the rumor tracing accuracy can reach more than 25%, and there is nearly 80% probability to find the rumor source point within one step, and about 98% probability to find the rumor source point within two steps. At the same time, the rumor tracing accuracy of the IDC algorithm is higher than that of the other three algorithms in the BA network and the LastFM network, and is slightly lower than that of the CC algorithm in the WS network, but is much higher than that of the RP and BC algorithms. In the Facebook network, the rumor tracing effect is weaker than that of the other three algorithms, but it can guarantee to find the rumor source point within 3.

[0058] The average error distance of different algorithms in different network topologies is compared, as shown in Figure 3

[0059] It can be seen that in the BA network, the rumor tracing algorithm based on infection depth aggregation (IDC) has the smallest average error distance, which is better than the other three rumor tracing algorithms. In the WS network, the average error distance of the IDC algorithm is slightly larger than 1 step, only second to the CC algorithm; except for the RP algorithm, the average error distances of the other three algorithms are all around 1 step, and the rumor tracing performance is better. In the LastFM network, the average error distances of several rumor tracing algorithms are very close, all around 2 steps. In the Facebook network, the average error distance of the IDC algorithm is higher than that of the other three algorithms, and the performance is relatively weak. However, no matter in which topology structure, the average error distance of the IDC algorithm is within 2 steps.

[0060] Further, the evolution process of the average error distance of the rumor tracing method based on infection depth aggregation in different network topology structures with the change of infection scale is shown in Figure 4 The evolution process of the average error distance with the change of the average degree of the network is shown in Figure 5 The change trend of the average error distance under different infection rates in different network topology structures is shown in Figure 6

[0061] It can be seen that the method of the embodiment can identify the rumor source point within several steps, and the comprehensive rumor tracing accuracy and average error distance in different network topologies are excellent and stable.

[0062] In the embodiment of the application, an electronic device is also provided, including: one or more processors; a storage device having one or more programs stored thereon; and when the one or more programs are executed by the one or more processors, the one or more processors implement the rumor tracing method based on infection depth aggregation described in any of the above embodiments.

[0063] ​​In the embodiments of the present application, a computer readable storage medium is also provided, and the computer readable storage medium stores a computer program. When the computer program is executed by a processor, the steps in any of the infection depth aggregation-based tracing methods in the above embodiments are implemented.

[0064] The above merely describes the preferred embodiments of the present application, and it should be noted that those skilled in the art can make several improvements and refinements without departing from the principles of the present application, and these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A rumor tracing method based on infection depth aggregation, characterized in that, The method comprises the following steps: S1, obtaining an infection subgraph based on an infection base graph network before and after rumor propagation, and calculating the infection degree of each node in the infection subgraph; Assume that the infection base graph before and after rumor spreading is G(V, E), and the infection subgraph is G i (V i ,E i ), any node Calculate the infection degree Inr(v) of node v, as follows: where V, V i respectively represent the set of nodes in the network before and after infection, E, E i respectively represent the set of edges in the network before and after infection, k v respectively represent the degree of node v in the infected subgraph and the infected base graph; if Inr(v) = 1, add node v to the suspicious set S of rumor sources. S2, traversing the nodes of the infection subgraph, and adding the nodes with an infection degree of 1 to a suspicious set of rumor source points; S3, traversing the suspicious set of rumor source points, performing a breadth-first search on the nodes in the suspicious set, and calculating the infection depth of each node; traversing the nodes in the suspicious set S, performing a breadth-first search BFS(G, v) on any node v e S, finding a first node u satisfying Inr(u) < 1, and calculating the infection depth from the node v, according to the following formula: dep i (v) = d(v, u) - 1 wherein d(v, u) is the shortest path length from the node v to the node u; S4, calculating the aggregation degree of the nodes in the suspicious set, and adding the aggregation degree set of the suspicious source points; for any node v e S in the suspicious set, calculating the infection aggregation degree Cr(v) of the node v according to the following formula: where dep i (v) represents the infection depth from node v, g i l (v) represents the number of infected nodes at a distance of l steps from node v; the step length l is set according to the shortest path length from node v; the infection aggregation degree of each node is calculated, the infection aggregation degree set C of suspected source points is added, and the node with the largest infection aggregation degree in C is selected as a suspected rumor source point; S5, selecting the node with the largest infection aggregation degree from the aggregation degree set of the suspicious source points as the presumed rumor source point.

2. An electronic device, comprising: comprise: one or more processors; a storage device having one or more programs stored thereon; when the one or more programs are executed by the one or more processors, the one or more processors implement the rumor tracing method based on infection depth aggregation as claimed in claim 1.

3. A computer-readable storage medium, characterized in that, a computer program is stored thereon, which is executed by a processor to implement the steps in the rumor tracing method based on infection depth aggregation as claimed in claim 1.

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