CN114826870A - Rumor propagation source tracing method based on multilayer social platform - Google Patents
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- H—ELECTRICITY
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Abstract
The invention discloses a rumor propagation source tracing method based on a multilayer social platform, which comprises the following steps: constructing a multi-layer social network, selecting observation points in advance, monitoring the number of observation points of received rumors in real time, giving a label value to each node, performing a label value iteration process, judging whether the label value is converged, selecting a propagation source according to the label value, and analyzing a test result; according to the method and the device, the real multi-channel propagation path is recorded through the observation point on the multi-layer social network, so that the problem that the randomness of the propagation path is high under the condition of low propagation rate is solved, and the source tracing precision of rumor propagation is improved.
Description
Technical Field
The invention belongs to the field of intelligent methods and propagation dynamics, and particularly relates to a rumor propagation source tracing method based on a multilayer social platform.
Background
For a social platform, the propagation process of rumors on it can be simulated by SI (separable-fed) model and SIR (separable-fed-Recovery) model. For the SI model, each node in the initial network is in an uninfected state, i.e., the rumor is not known, and from a certain moment, one or more nodes in the network start to propagate the rumor to the nodes in contact with the node, and the nodes which originally propagate the rumor are considered as propagation sources; at each subsequent time, the infected node spreads the rumor to the nodes with which it is in contact, and an uninfected node is infected with a probability p. For the SIR model, the difference from the SI model is that the node can "recover" (Recovery), i.e. an infected node verifies that the message is a rumor and therefore does not trust the message and participate in the propagation process.
For a single system, i.e., a single-tier social network; pinto et al propose locating the propagation source (Physical Review Letters) by arranging "observation points" in the network to record the time of infection, by: firstly, selecting a part of nodes in a network as observation points, recording infection time of the nodes after rumors start to spread and recording the infection time as an observation time vector d; in the positioning process, traversing each node in the network, assuming that the node is a propagation source and triggers the propagation process, and forming another theoretical time vector mu by the time when the rumor propagates to each observation point; the similarity of the two vectors is calculated by using a multivariate normal distribution probability density function, so that the node with the highest similarity is regarded as a propagation source. Wang et al propose a method of using a tag value iteration to locally maximize a tag value of a central node of an infected area based on a propagation Source Centrality principle (Source Centrality) to locate a propagation Source (third-First AAAI Conference on Intelligent intellectual insight), which comprises the following steps: when a certain proportion of nodes in the network are infected, the propagation is stopped, the network snapshot at the moment is obtained, and a label value is given to each node according to the state of each node (the infected node is given +1, and the non-infected node is given-1). And traversing each node in the network to perform a label value iteration process until the label value of each node in the network is not changed any more, and selecting the node with the maximum local label value as a propagation source.
Most existing propagation source locating methods are directed to a single social platform, however, many different social media in the real world are interdependent and interinfluential, such as: one person may have different social accounts (e.g., WeChat and QQ, Facebook and Twitter, etc.); a person may play different roles in different social circles (e.g., family, friends, colleagues, etc.). Therefore, the multi-channel propagation feature on the social network platform makes the propagation source positioning problem more research and practical.
The social networking multi-channel interaction platform can be abstracted into a form of a 'multiplex network'. A Multiplex network is a special form of a multi-layer network in which the topology of different layers may be different, each layer containing the same nodes and the nodes between different layers being in one-to-one correspondence. For multi-layer networks, Paluch et al extend the method of multivariate normal distribution probability density function similarity calculation to multiple layers (Physica: Statistical Mechanics and its Applications). The difference from the positioning method on a single layer is that: they considered the interlayer propagation and analyzed the effect of the interlayer infection rate on the localization results.
Although many methods have been proposed for rumor propagation source tracing, the existing methods suffer from the following disadvantages:
1. most of the existing methods are directed at a single-layer network, and the positioning method of a propagation source on multiple layers is rarely researched;
the method proposed by Paluch et al can only be used for single propagation source positioning, and cannot be used for solving source tracing positioning under the condition of multiple propagation sources;
3. the probability of rumor propagation in the real world is relatively low, and the propagation path has high randomness; this makes the existing method less applicable.
Disclosure of Invention
The invention aims to provide a rumor propagation source tracing method based on a multi-layer social platform, so as to solve the problems in the prior art.
In order to achieve the above object, the present invention provides a rumor propagation source tracing method based on a multi-layer social platform, including:
constructing a multi-layer social network; selecting random nodes in the multilayer social network as observation points to record rumor propagation conditions, and endowing initial label values to all nodes based on the rumor propagation conditions;
iterating the initial label value to obtain an iterative label value; an infection source location is obtained based on the iterative tag value.
Optionally, the multi-layer social network includes several layers of different social platforms, and the construction method of the multi-layer social network is as follows:
setting each user in the social platform as a node, setting the connection between the nodes as an edge, constructing a connection edge between the same nodes on different social platforms, constructing a connection edge on the same social platform based on the connection between different nodes, and constructing the multilayer social network based on the nodes and the constructed connection edge.
Optionally, the method for constructing a multi-layer social network further includes:
there are several neighbors around each node, including: nodes having connecting edges at the same level as the nodes and nodes having connecting edges at different levels from the nodes.
Optionally, the selecting a random node as an observation point in the multi-layer social network to record the propagation condition of the rumor includes:
upon reception of a rumor, recording the location of the rumor spreader based on the observation point and taking a network snapshot of the multi-tiered social network at the rumor outbreak moment.
Optionally, the network snapshot includes: topology of the multi-tiered social network, status of each node in the multi-tiered social network.
Optionally, the method for assigning the initial tag value is as follows: nodes that have received rumors are assigned +1, nodes that have not received rumors are assigned-1.
Optionally, in the process of obtaining the iterative label value, performing calculation based on the propagation condition of the rumor between each node and the neighbor, and performing iteration after obtaining a calculation result.
Optionally, the calculating based on propagation of rumors between each node and neighbors comprises:
in the formula (1), alpha is (0,1), beta is (alpha, 1), T j Representing the label value, W, that node i gets from neighbor j ij The element corresponding to the ith row and the jth column in the matrix W is shown, the structural mode of the matrix W is shown in an expression (2),
a label value representing node j at time t,
W＝D -1/2 AD -1/2 (2)
a in the formula (2) is a super-adjacent matrix of the multiplex network; if the node i and the node j are mutually connected, the element of the ith row and the jth column in the A is 1, otherwise, the element is 0; d is a degree matrix of A, and the element of the ith row and the ith column in D is equal to the sum of the number of the elements of the ith row of A, which are not zero;
equation (1) represents the label value obtained by node i from the neighbor at time t, while node i will retain a part of its initial label value, and then the label value of node i at time t +1 is represented by equation (3):
in the formula (3)
Represents the label value of node i at time t +1, n (i) represents all neighbors of node i in the network, j: j ∈ N (i) represents all neighbors that traverse node i on the network, ∑ j：j∈N(i) T j Meaning that node i is derived from all its neighbor nodes jTag value of (T) j The value range of the parameter alpha is (0,1), Y i Indicating the initial label value assigned to node i, i.e., +1 or-1.
Optionally, the process of performing iteration after obtaining the calculation result includes: and traversing all the nodes in each iteration, stopping the iteration when the iteration label values of all the nodes in the network stop changing, and otherwise, continuously executing the iteration.
Optionally, after the iterative label value stops changing, traversing all nodes in the social network, and for each node, if the following conditions are satisfied, determining that the node is a rumor propagation source:
when the transmission is stopped, the initial label value of the node is positive 1;
in single-source positioning, judging the node with the maximum iteration label value as a rumor propagation source;
in the multi-source positioning, the nodes with iteration label values larger than all the neighbors are judged to be rumor propagation sources.
The invention has the technical effects that:
according to the method and the device, the real multi-channel propagation path is recorded through the observation point on the multi-layer social network, so that the problem that the randomness of the propagation path is high under the condition of low propagation rate is solved, and the source tracing precision of rumor propagation is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:
FIG. 1 is a general flow chart of a method in an embodiment of the invention;
FIG. 2 is a schematic diagram of a double-layer multiplex network and its super-adjacency matrix according to an embodiment of the present invention;
FIG. 3 is a schematic view of the direction of label value flow through a viewpoint in an embodiment of the present invention;
FIG. 4 is a process diagram of a first embodiment of the present invention;
FIG. 5 is a diagram of the result of the single source positioning of the method on the Citeser network;
fig. 6 is a result diagram of multi-source positioning on the cineser network by the method.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
Example one
As shown in fig. 1-6, the present embodiment provides a rumor propagation source tracing method based on a multi-layer social platform, including:
the specific implementation process of the model is as follows:
s1, constructing a multi-layer social network:
for different social platforms containing the same node, the node on each social platform is abstracted into one node, the connection between the nodes is abstracted into edges, and the same node on different social platforms is connected by one edge by default. It is specified here that the neighbours of a node i include: and the nodes with connecting edges at the same layer as the i and the nodes with connecting edges at different layers from the i, namely all the nodes with connecting edges with the i in the network.
S2, selecting observation points in advance:
before rumors are not exploded, a certain proportion (theta%) of nodes are randomly selected as observation points in the network, and it should be noted that the same node on different layers may be simultaneously selected as observation points, because message forwarding situations of different social accounts of the same node can be investigated in reality. These observation points record the rumor's spreader ID address when the rumor was received.
S3, monitoring the number of observation points for collecting rumors in real time:
after rumor outbreak, when η% observation points in the network receive rumors, a network snapshot is obtained, and the snapshot comprises the following information: the topology of the network, the status of each node in the network (whether rumor was received), and the rumor spreader ID address recorded by the observation point.
S4, endowing each node with a label value:
the node that has received the rumor is assigned with +1, and the node that has not received the rumor is assigned with-1.
S5, label value iteration process:
after the nodes are endowed with label values, a label value iteration process is carried out, all the nodes are traversed in each iteration, and for different relations among the nodes, an iteration rule is specified as follows:
for node i and its neighbors j; if j is an observation point and he is infected by i, node i will get the label value corresponding to case 1 in equation (1) from node j.
On the premise that 1) does not hold, if i is not an observation point, or i is an observation point but not infected by j. Node i gets the label value from j for case 2 in equation (1).
If neither of the cases 1) and 2) above is true, node i gets a tag value of 0 from node j, which is case 3 in equation (1).
In summary, the label value that node i gets from his neighbor j can be summarized by:
in the formula (1), alpha is (0,1), beta is (alpha, 1), T j Representing the label value, W, that node i gets from its neighbor j ij The element corresponding to the ith row and the jth column in the matrix W is shown, the structural mode of the matrix W is shown in an expression (2),
a label value representing node j at time t,
W＝D -1/2 AD -1/2 (2)
in the formula (2), a is a super-adjacency matrix of the multiplex network, and the super-adjacency matrix of the multiplex network is shown in fig. 2 (b); if the node i and the node j can be mutually connected, the element of the ith row and the jth column in the A is 1, otherwise, the element is 0; d is a degree matrix of A and is also a diagonal matrix, and the element of the ith row and ith column in D is equal to the sum of the number of elements which are not zero in the ith row of A.
Equation (1) represents the label value that node i gets from its neighbors at time t, while node i will retain a portion of its initial label value, then the label value of node i at time t +1 can be represented by equation (3):
in formula (3)
A label value representing node i at time t + 1, and n (i) representing all neighbors of node i in the network (the neighbors of a node are defined in S1), where j: j ∈ N (i) means that all neighbors of node i on the network are traversed, ∑ j：j∈N(i) T j Representing the label value T that node i gets from all its neighbor nodes j j The value range of the parameter alpha is (0,1), Y i Indicating the initial label value assigned to node i, i.e., +1 or-1.
S6, judging whether the label value is converged or not
The iterative process is stopped when the label values of all nodes in the network converge, i.e., do not change, otherwise the step S5 is continued.
S7, selecting a propagation source according to the label value:
traversing all nodes in the network after the label value is converged, and regarding each node i, if i meets the following conditions, considering i as a propagation source obtained by positioning:
Y i the initial tag value of node i is positive 1, which indicates that i is in an infected state when propagation is stopped.
In single source positioning, G i Is the largest of all nodes; i.e. the label value of node i is allThe largest of the nodes.
In multi-source localization, the label value G of node i i Greater than the tag values of all of his neighbors.
S8, testing and analyzing:
the test verifies the effectiveness of the model by comparing with the prior method. The test method is chosen as the propagation source localization method on the multilayer network mentioned in the first section. First it is defined that S is the true set of propagation sources,
is the set of located propagation sources. Two commonly used propagation source location methods are described herein, namely Error Distance (Error Distance) and F-Score; and evaluating single-source positioning by using the error distance, and specifying the length of the shortest path between the propagation source obtained by the bits and the actual propagation source on the network G, wherein the path on the multilayer network is formed by an intra-layer path and an inter-layer path together, and since each layer in the multiplex network comprises the same node, the length of the inter-layer path is set to be 0. The multi-source localization was evaluated by F-Score index, the formula was calculated as (4), where Precision represents
The proportion of true propagation sources in the set, Recall, represents the proportion of nodes in the S set that are correctly located, where γ is taken to be 0.5.
And (3) analyzing a test result: whether single source or multi-source localization, the accuracy of localization improves with increasing infection rate or increasing ratio of observation points. In single source localization, the method reduces the error distance by about 22% based on the observation point-based method of recording the time of infection.
The above description is only for the preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A rumor propagation source tracing method based on a multi-layer social platform is characterized by comprising the following steps:
constructing a multilayer social network, selecting random nodes in the multilayer social network as observation points to record rumor propagation conditions, and endowing initial label values to all nodes based on the rumor propagation conditions;
and iterating the initial label value to obtain an iterative label value, and obtaining the infection source position based on the iterative label value.
2. The method of claim 1, wherein the multi-layered social network comprises several layers of different social platforms, and the multi-layered social network is constructed by:
setting each user in the social platform as a node, setting the connection between the nodes as an edge, constructing a connection edge between the same nodes on different social platforms, constructing a connection edge on the same social platform based on the connection between different nodes, and constructing the multilayer social network based on the nodes and the constructed connection edge.
3. The method of claim 2, wherein the method for constructing the multi-tiered social network further comprises:
there are several neighbors around each node, including: nodes having connecting edges at the same level as nodes and nodes having connecting edges at different levels as nodes.
4. The method of claim 1, wherein the selecting random nodes as observation points in the multi-layered social network to record rumor propagation source tracing comprises:
upon reception of a rumor, recording the location of the rumor spreader based on the observation point and taking a network snapshot of the multi-tiered social network at the rumor outbreak moment.
5. The multi-tiered social platform-based rumor propagation sourcing method of claim 4, wherein the network snapshot comprises: topology of the multi-tiered social network, status of each node in the multi-tiered social network.
6. The method of claim 1, wherein the initial tag value is assigned by: nodes that have received rumors are assigned +1, nodes that have not received rumors are assigned-1.
7. The method of claim 1, wherein iteration is performed on the initial label value, and in the process of obtaining the iterative label value, calculation is performed based on the propagation condition of the rumor between each node and a neighbor, and iteration is performed after a calculation result is obtained.
8. The method of claim 7, wherein computing based on propagation of rumors between each node and neighbors comprises:
in the formula (1), alpha is (0,1), beta is (alpha, 1), T j Representing the label value, W, that node i gets from neighbor j ij The element corresponding to the ith row and the jth column in the matrix W is shown, the structural mode of the matrix W is shown in an expression (2),
a label value representing node j at time t,
W＝D -1/2 AD -1/2 (2)
a in the formula (2) is a super-adjacent matrix of the multiplex network; if the node i and the node j are mutually connected, the element of the ith row and the jth column in the A is 1, otherwise, the element is 0; d is a degree matrix of A, and the element of the ith row and the ith column in D is equal to the sum of the number of the elements of the ith row of A, which are not zero;
equation (1) represents the label value obtained by node i from the neighbor at time t, while node i will retain a part of its initial label value, and then the label value of node i at time t +1 is represented by equation (3):
in the formula (3)
Represents the label value of node i at time t +1, n (i) represents all neighbors of node i in the network, j: j ∈ N (i) represents all neighbors that traverse node i on the network, ∑ j：j∈N(i) T j Representing the label value T that node i gets from all its neighbor nodes j j The value range of the parameter alpha is (0,1), Y i Indicating the initial label value assigned to node i, i.e., +1 or-1.
9. The method of claim 7, wherein the iteration process after obtaining the calculation result comprises: and traversing all the nodes in each iteration, stopping the iteration when the iteration label values of all the nodes in the network stop changing, and otherwise, continuously executing the iteration.
10. The method of claim 9, wherein after the iterative label value stops changing, all nodes in the social network are traversed, and for each node, if the following conditions are satisfied, the node is determined to be a rumor propagation source:
when the transmission is stopped, the initial label value of the node is positive 1;
in single-source positioning, judging the node with the maximum iteration label value as a rumor propagation source;
in the multi-source positioning, the nodes with iteration label values larger than all the neighbors are judged to be rumor propagation sources.
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