CN109120431B - Method and device for selecting propagation source in complex network and terminal equipment - Google Patents

Abstract

The invention is applicable to the technical field of complex network data mining, and provides a method, device and terminal equipment for selecting a propagation source in a complex network. Determine the initial propagation source node; then calculate the comprehensive influence between the initial propagation source nodes according to the overlapping influence between the initial propagation source nodes in the propagation source set, select the node with the largest comprehensive influence as the propagation source node, and add In the propagation source set; then update the propagation source node according to the influence of a single node in the propagation source set; until the number of nodes in the propagation source set reaches the preset number. The invention takes into account the overlapping influence in the propagation source nodes, and deletes the nodes with low influence of a single node in the dense propagation source nodes, so that the propagation source nodes are distributed more evenly, and the information can be propagated more efficiently.

Description

Method, device and terminal equipment for selection of propagation source in complex network

technical field

The invention belongs to the technical field of complex network data mining, and in particular relates to a method, a device and a terminal device for selecting a propagation source in a complex network.

Background technique

Many things in the real world are related to each other, and they can usually be expressed in the form of complex networks, such as social networks, paper co-authorship networks, and communication networks. There is often information dissemination on these networks, so in order to suppress or promote the process of dissemination, it is usually necessary to analyze the dissemination capabilities of nodes in the network, and select the source node that can maximize the dissemination effect.

In the process of selecting the source node of the propagation in the traditional method, the judgment is generally based on the influence index of a single node, such as degree, betweenness, PageRank, etc. However, in this method, the nodes with high influence are usually very closely distributed, and the propagation source node The mutual propagation between them is meaningless, so this has a certain negative impact on the propagation efficiency, resulting in low propagation efficiency for selected multi-source nodes in complex networks.

SUMMARY OF THE INVENTION

In view of this, embodiments of the present invention provide a method, apparatus, and terminal device for selecting a propagation source in a complex network, so as to solve the problem of low propagation efficiency of multiple propagation source nodes selected in a complex network in the prior art.

A first aspect of the embodiments of the present invention provides a method for selecting a propagation source in a complex network, including:

Step S1: preprocessing the complex network;

Step S2: Calculate the influence of each node in the processed complex network, and use the node with the highest influence as the initial propagation source node and add it to the propagation source set;

Step S3: according to the overlapping influence between the initial propagation source nodes in the propagation source set, respectively calculate the comprehensive influence between the initial propagation source nodes, and select the node with the largest comprehensive influence as the propagation source node, and add it to the set of propagation sources;

Step S4: Calculate the single-node influence of the newly added propagation source node and each node in the propagation source set, delete the node whose single-node influence is smaller than the newly added propagation source node in the propagation source set, and update the set of propagation sources;

Step S5: Determine whether the number of nodes in the set of propagation sources is less than the preset number of propagation sources, if so, return to step S3; if not, take the set of propagation sources as a result and return.

In combination with the first aspect of the present invention, in the first embodiment of the first aspect of the present invention, the preprocessing of the complex network includes:

Delete isolated nodes and small node clusters in the complex network.

With reference to the first aspect of the present invention, in the second embodiment of the first aspect of the present invention, the calculating the influence of each node in the processed complex network includes:

Calculate the degree of each node in the processed complex network, the betweenness of each node, or use the page ranking algorithm PageRank to calculate the importance of each node.

With reference to the first aspect of the present invention, in the third implementation manner of the first aspect of the present invention, the calculation of the comprehensive influence between each node in the processed complex network and the set of propagation sources includes:

The pre-defined comprehensive influence calculation formula is:

表示综合影响力，S表示传播源集合，w_i表示节点i的影响力， w_ij表示节点i和j的重叠影响力，其中所述节点i的影响力包括节点i的度、介数或者PageRank，所述i节点和j的重叠影响力包括节点i和j之间边的度、介数或者PageRank；in,

represents the comprehensive influence, S represents the set of propagation sources, wi represents the influence of node _{i, and w ij represents} the overlapping influence of nodes i and j, wherein the influence of node i includes the degree, betweenness or PageRank of node i , the overlapping influence of the i node and j includes the degree, betweenness or PageRank of the edge between nodes i and j;

The comprehensive influence between each node in the processed complex network and the set of propagation sources is calculated according to the comprehensive influence formula.

With reference to the first aspect of the present invention, in the fourth embodiment of the first aspect of the present invention, the calculating the single-node influence of the newly added propagation source node and each node in the propagation source set includes:

The influence of a single node is pre-defined, and the calculation formula is:

Among them, Pi represents the single-node influence of node _i , S represents the set of propagation sources, wi represents the influence of node _{i, and w ij represents} the overlapping influence of nodes i and j, wherein the influence of node i includes nodes The degree, betweenness or PageRank of i, the overlapping influence of nodes i and j includes the degree, betweenness or PageRank of the edge between nodes i and j;

The single-node influence of the newly added propagation source node and each node in the propagation source set is calculated according to the single-node influence formula.

A second aspect of the embodiments of the present invention provides an apparatus for selecting a propagation source in a complex network, including:

The preprocessing module is used to preprocess the complex network;

The first selection module is used to calculate the influence of each node in the processed complex network, and the node with the highest influence is used as the initial propagation source node and added to the propagation source set;

The second selection module is configured to calculate the comprehensive influence between each node in the processed complex network and the set of propagation sources, select the node with the highest comprehensive influence as the propagation source node and join the propagation source in the collection;

Screening and updating module, used to calculate the single-node influence of the newly added propagation source node and each node in the propagation source set, and delete the node whose single-node influence is less than the newly added propagation source node in the propagation source set , and update the set of propagation sources;

A judging module, configured to judge whether the number of nodes in the set of propagation sources is less than the preset number of propagation sources, if so, return to the second selection module, if not, take the set of propagation sources as a result and return.

With reference to the second aspect of the present invention, in the first embodiment of the second aspect of the present invention, the preprocessing of the complex network includes:

Delete isolated nodes and small node clusters in the complex network.

With reference to the second aspect of the present invention, in the second embodiment of the second aspect of the present invention, the calculating the influence of each node in the processed complex network includes:

Calculate the degree of each node in the processed complex network, the betweenness of each node, or use the page ranking algorithm PageRank to calculate the importance of each node.

A third aspect of the embodiments of the present invention provides a terminal device for selecting a propagation source in a complex network, including a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor executes the above The computer program implements the steps of the method as provided in the first aspect above.

A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, implements the steps of the method provided in the first aspect.

The beneficial effects that the embodiment of the present invention has compared with the prior art are:

The present invention first preprocesses the complex network; then calculates the influence of each node in the complex network after complex processing to determine the initial propagation source node; then according to the overlapping influence between the initial propagation source nodes in the propagation source set, respectively Calculate the comprehensive influence between the initial propagation source nodes, select the node with the largest comprehensive influence as the propagation source node, and add it to the propagation source set; then delete the node whose influence of a single node in the propagation source set is smaller than that of the newly added propagation source node; Determine whether the number of nodes in the propagation source set reaches the preset number, and if not, continue to calculate the comprehensive influence to add new nodes to the propagation source set until the preset number is reached. Compared with the prior art, the present invention takes into account the overlapping influence among the propagation source nodes, and deletes the nodes with low influence of a single node in the densely placed propagation source nodes, so that the propagation source nodes are distributed more evenly, and the information more efficient dissemination.

Description of drawings

1 is a schematic flowchart of the implementation of a method for selecting a propagation source in a complex network according to Embodiment 1 of the present invention;

2 is a schematic diagram of the realization effect of the method for selecting a propagation source in a complex network according to Embodiment 2 of the present invention;

3 is a schematic diagram of a first measurement index of a method for selecting a propagation source in a complex network according to Embodiment 3 of the present invention;

4 is a schematic diagram of a second measurement index of the method for selecting a propagation source in a complex network according to Embodiment 3 of the present invention;

5 is a schematic diagram of a third measurement index of the method for selecting a propagation source in a complex network according to Embodiment 3 of the present invention;

6 is a schematic diagram of a fourth measurement index of the method for selecting a propagation source in a complex network according to Embodiment 3 of the present invention;

FIG. 7 is a schematic structural diagram of an apparatus for selecting a propagation source in a complex network according to Embodiment 4 of the present invention.

The realization, functional characteristics and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments.

Detailed ways

It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

It should be noted that, herein, the terms "comprising", "comprising" or any other variation thereof are intended to encompass non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements, It also includes other elements not expressly listed or inherent to such a process, method, article or apparatus. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in a process, method, article or apparatus that includes the element.

Herein, suffixes such as "module", "component" or "unit" used to denote elements are used only to facilitate the description of the present invention and have no specific meaning per se. Therefore, "modules" and "parts" can be mixed.

In the following description, the serial numbers of the embodiments of the invention are only for description, and do not represent the advantages and disadvantages of the embodiments.

Example 1

As shown in FIG. 1 , an embodiment of the present invention provides a method for selecting a propagation source in a complex network, which is characterized by comprising:

Step S1: Preprocess the complex network.

In the above step S1, any network can produce and publish information, and all the information produced and published by the network can flow into the network in a non-linear manner.

In the embodiment of the present invention, define a complex network A={a _ij } _N×N , a _ij ={0,1}, where A represents the adjacency matrix of the complex network, N is the size of the complex network, and i and j represent the two a _ij =1 when i, j have an edge, otherwise a _ij =0. Define the number of propagation sources as L.

In one embodiment, the preprocessing of the complex network includes: deleting isolated nodes and small node clusters in the complex network.

In specific applications, isolated nodes in complex networks can be obtained by any method that can obtain isolated nodes, for example, by calculating the equation satisfied by isolated nodes; small node clusters in complex networks can be obtained by any method that can obtain small nodes. The method of node clusters is obtained, for example, by calculating the distance between nodes; after deleting isolated nodes and small node clusters in complex networks, the largest connected subgraph in the network can be retained, and the 4-core data of the network can be extracted. The 4-core data includes but is not limited to the number of 4-core network nodes and the number of 4-core network edges.

Step S2: Calculate the influence of each node in the processed complex network, and use the node with the highest influence as the initial propagation source node and add it to the propagation source set.

In the above step S2, according to the importance index of the node, the importance of the node and the importance of the edge are calculated based on degree, betweenness, PageRank, etc., and the propagation source is initialized with the node with the highest importance.

In one embodiment, the calculating the influence of each node in the processed complex network comprises: calculating the degree of each node in the processed complex network, the betweenness of each node, or using a web page The ranking algorithm PageRank calculates the importance of each node.

In specific applications, Degree Centrality (DC), referred to as degree, is one of the simplest measures. Assuming that the number of nodes in a network is N, the maximum possible degree value of a node is N-1, and the degree centrality of a node with degree K _i is defined as:

Among them, if there are in-degree and out-degree points in a directed network, the centrality is calculated separately at this time. Degree centrality only considers the surrounding nodes of a node and is only a locality feature.

In specific applications, betweenness centrality (Betweenness Centrality, BC), referred to as betweenness, is divided into node betweenness centrality and edge betweenness centrality, node betweenness is defined by the formula:

为从节点s到节点t的 g_st条最短路径中经过节点i的最短路径的数目。where _gst is the number of shortest paths from node s to node t,

is the number of shortest paths passing through node i among the g _st shortest paths from node s to node t.

The edge betweenness is defined by the formula:

为从节点s到节点t的最短路径中经过边e的数目。边介数衡量了边的连通能力。

is the number of edges e in the shortest path from node s to node t. Edge betweenness measures the connectivity of edges.

In specific applications, page ranking algorithm (PageRank, PR) is widely used in node importance ranking. Calculated as follows:

Step S3: according to the overlapping influence between the initial propagation source nodes in the propagation source set, respectively calculate the comprehensive influence between the initial propagation source nodes, and select the node with the largest comprehensive influence as the propagation source node, and added to the set of propagation sources.

In the above-mentioned steps S2 and S3, the node with the highest influence is used as the initial propagation source node and added to the propagation source set, and the node with the highest comprehensive influence is used as the propagation source node and added to the propagation source set to complete the propagation source set. data initialization.

In one embodiment, the separately calculating the comprehensive influence between each node in the processed complex network and the set of propagation sources includes:

The comprehensive influence is pre-defined, and the calculation formula is:

表示综合影响力，S表示传播源集合，w_i表示节点i的影响力， w_ij表示节点i和j的重叠影响力，其中所述节点i的影响力包括节点i的度、介数或者PageRank，所述i节点和j的重叠影响力包括节点i和j之间边的度、介数或者PageRank；in,

represents the comprehensive influence, S represents the set of propagation sources, wi represents the influence of node _{i, and w ij represents} the overlapping influence of nodes i and j, wherein the influence of node i includes the degree, betweenness or PageRank of node i , the overlapping influence of the i node and j includes the degree, betweenness or PageRank of the edge between nodes i and j;

The comprehensive influence between each node in the processed complex network and the set of propagation sources is calculated according to the comprehensive influence formula.

In the specific application, in the degree-based method, w _i represents the degree of node i, if there is an edge between i and j, then w _ij =1, otherwise w _ij =0; in the betweenness-based method, w _i represents the node The betweenness of i, w _ij represents the betweenness of the edge; based on the PageRank method, set the number of iterations to 5, first calculate w _i , initialize PR _i (0)=1 and PR _{j, j≠i} (0)=0, In each iteration process, the PageRank value corresponding to each node is calculated according to the PageRank formula and PR _i (k)=1, k=1, 2, 3, and 4 are re-initialized. The final _wi is the sum of the PageRank values of the non-propagating source nodes. When there is an edge between nodes i, j, calculate w _ij , initialize PR _i (0)=PR _j (0)=1 and PR _{m, m≠i, j} (0)=0, calculate in each iteration process The PageRank value corresponding to each node, and also re-initialize PR _i (k)=PR _j (k)=1, k=1, 2, 3, 4, the final w _ij is the sum of the PageRank values of the non-propagating source nodes and.

Step S4: Calculate the single-node influence of the newly added propagation source node and each node in the propagation source set, delete the node whose single-node influence is smaller than the newly added propagation source node in the propagation source set, and update the set of propagation sources.

In the above step S4, each time a new propagation source node is added, the single influence size of each propagation source node is calculated and compared. If there is a node in the propagation source set whose influence is smaller than that of the newly added node, then Delete the node.

In one embodiment, the calculating the single-node influence of the newly added propagation source node and each node in the propagation source set includes:

The influence of a single node is pre-defined, and the calculation formula is:

Among them, Pi represents the single-node influence of node _i , S represents the set of propagation sources, wi represents the influence of node _{i, and w ij represents} the overlapping influence of nodes i and j, wherein the influence of node i includes nodes The degree, betweenness or PageRank of i, the overlapping influence of nodes i and j includes the degree, betweenness or PageRank of the edge between nodes i and j;

The single-node influence of the newly added propagation source node and each node in the propagation source set is calculated according to the single-node influence formula.

Step S5: Determine whether the number of nodes in the set of propagation sources is less than the preset number of propagation sources, if so, return to step S3; if not, take the set of propagation sources as a result and return.

In the above step S5, when the number of propagation sources reaches the set number and reaches the set value, such as L, the selected propagation source node corresponds to the index of the original network, and returns. However, as the number of source nodes increases, the overlapping influence becomes higher. At this time, information cannot be spread quickly, so it is necessary to reduce the overlapping influence between nodes.

The method for selecting a propagation source in a complex network provided by the embodiment of the present invention firstly preprocesses the complex network; then calculates the influence of each node in the complex network after complex processing to determine the initial propagation source node; then according to the propagation source set Calculate the overlapping influence between the initial propagation source nodes in the initial propagation source nodes, respectively calculate the comprehensive influence between the initial propagation source nodes, select the node with the largest comprehensive influence as the propagation source node, and add it to the propagation source set; then delete the propagation source set. The influence of a single node is smaller than that of the newly added propagation source node; judge whether the number of nodes in the propagation source set reaches the preset number, if not, continue to calculate the comprehensive influence to add new nodes to the propagation source set until the predetermined number is reached. until the number is set. Compared with the prior art, the present invention takes into account the overlapping influence among the propagation source nodes, and deletes the nodes with low influence of a single node in the densely placed propagation source nodes, so that the propagation source nodes are distributed more evenly, and the information more efficient dissemination.

Embodiment 2

As shown in FIG. 2 , the embodiment of the present invention also provides the method for selecting a propagation source in a complex network described in steps S1 to S5 in the first embodiment, and describes the effect in practical application. Nodes 1 to 7 and 12 marked in black are nodes with large degrees, while nodes 8 to 11 and nodes 13 to 15 marked in gray are nodes with small degrees. Assuming that the number of propagation sources is 4, that is, 4 nodes are selected as propagation sources. In the traditional method based on degree, nodes 1, 2, 3, and 4 should be selected first. Obviously, the information cannot be spread quickly because the degree is small. The distance between the node and the propagation source node is slightly farther, and the internal connection between the propagation source nodes is tight. When reducing the overlapping influence between the propagation source nodes, that is, selecting nodes 1, 4, 7, 12 with large degrees and sparse distribution, the information will soon spread throughout the network, and the set of propagation sources at this time The overlapping influence of mid-nodes is low.

Embodiment 3

The embodiments of the present invention are directed to the method for selecting a propagation source in a complex network provided in the first embodiment, and use data to illustrate the beneficial effects in its practical application.

In the embodiment of the present invention, four real networks are selected: facebook, CA-HepPh, Hamster, as-caida. According to the method in the first embodiment, an experiment of selecting a propagation source in a complex network is performed.

First, the network structure is regarded as weightless and undirected. During preprocessing, isolated nodes and small node clusters are deleted, only the largest connected subgraph is retained, and the 4-core data of the network is extracted. Due to the self-similarity of the network , the sub-network has similar structural characteristics to the original network, and the specific characteristics of the network are as follows:

In the embodiment of the present invention, several measurement indicators are proposed to prove the beneficial effect of the method for selecting a propagation source in a complex network provided in the first embodiment.

The first measure is the coverage of information spreading in the network, which reflects the spreading capability of the source node:

Among them, N _l and N _R represent the number of infected nodes and immune nodes respectively when the propagation reaches a steady state, and N is the total number of nodes.

As shown in FIG. 3 , the first measurement index proposed in the embodiment of the present invention shows that the propagation range corresponding to the method for selecting a propagation source in a complex network provided in Embodiment 1 is much higher than the first measurement index under the traditional method. The propagation range corresponding to a metric, especially in (a) facebook and (c) hamster-based PageRank methods, the improvement range is significantly improved, and the final propagation range is almost the entire network. Due to the small number of propagation source nodes at the beginning, the corresponding overlapping influence between propagation source nodes is relatively small. At this time, the effect of our improved method is not very obvious; however, as the number of propagation source nodes increases, the overlap The influence is relatively high, and the improvement effect of the improved method proposed in the first embodiment is also significantly increased.

In practical applications, two people who are far away have a small number of friends in common, while two people who are close often have more friends. Therefore, the average distance between propagation source nodes most easily reflects the overlapping influence between nodes. Therefore, a second measurement index d is proposed, which reflects the average distance between the propagation source nodes:

where L represents the number of propagation source nodes, and d _ij represents the distance between nodes i and j.

As shown in FIG. 4 , the second measurement index proposed in the embodiment of the present invention shows that according to the method for selecting a propagation source in a complex network provided in the first embodiment, the selected propagation source node has a significantly higher average distance, This just reflects the reduced overlapping influence of the propagation source nodes in the network. (c) The improvement of the betweenness method corresponding to the hamster network is not very obvious, which also reflects the similarity of the propagation ability of the (c) hamster network in Figure 3.

In practical applications, in addition to the average distance, the similarity of the propagation source nodes can also be used to characterize the overlapping influence. Generally speaking, nodes with low similarity often mean that the overlapping influence between nodes is low. Therefore, the third measure s is proposed to reflect the similarity of the propagation source nodes:

Among them, S _i and S _j represent the neighbor node sets of node i and node j, respectively.

As shown in FIG. 5 , the third measurement index proposed in the embodiment of the present invention shows that according to the method for selecting a propagation source in a complex network provided in the first embodiment, the similarity between nodes in the propagation source set embodied is in the , the similarity between nodes in the set of propagation sources obtained by the improved method through degree and PageRank is reduced. However, the similarity between the nodes of the method after betweenness improvement is very low, and there is no obvious change compared with the traditional method.

Combining Figure 4 and Figure 5, it can be seen that the propagation sources selected by the two betweenness methods are different, so similarity is not a good indicator for judging overlapping influence for the betweenness method, and the average distance can better reflect Overlapping influence between nodes.

Finally, the embodiment of the present invention proposes a fourth measurement index σ, which reflects the diversity of the selection of propagation source nodes through the intersection of the method for selecting propagation sources in the complex network provided in the first embodiment and the set of propagation source nodes selected by the traditional method:

Wherein, S ₁ is the set of propagation source nodes selected by the method for selecting a propagation source in a complex network provided in the first embodiment, and S ₂ is the set of propagation source nodes selected by the traditional method.

As shown in FIG. 6 , it reflects the intersection between the traditional method and the method for selecting a propagation source in a complex network provided in Embodiment 1 to select a set of propagation source nodes. For example (a) in the PageRank method corresponding to the facebook network, σ < 0.4, which indicates the diversity of the selection of the source nodes for propagation. In the betweenness method corresponding to the (c)hamster network, σ>0.8, which means that the propagation source nodes selected by the two methods are almost similar, which also explains that the two betweenness methods in Figure 4 have similar propagation capabilities. .

Embodiment 3

As shown in FIG. 7 , an embodiment of the present invention provides an apparatus 30 for selecting a propagation source in a complex network, including:

The preprocessing module 71 is used for preprocessing the complex network.

In one embodiment, the preprocessing of the complex network includes: deleting isolated nodes and small node clusters in the complex network.

The first selection module 72 is configured to calculate the influence of each node in the processed complex network, and use the node with the highest influence as the initial propagation source node and add it to the propagation source set.

In one embodiment, the calculating the influence of each node in the processed complex network comprises: calculating the degree of each node in the processed complex network, the betweenness of each node, or using a web page The ranking algorithm PageRank calculates the importance of each node.

The second selection module 73, according to the overlapping influence between the initial propagation source nodes in the propagation source set, respectively calculates the comprehensive influence between the initial propagation source nodes, and selects the node with the largest comprehensive influence as the propagation The source node is added to the set of propagation sources.

The screening and updating module 74 is used to calculate the single-node influence of the newly added propagation source node and each node in the propagation source set, and delete the single-node influence in the propagation source set that is less than the newly added propagation source node. node, and update the set of propagation sources.

The judging module 75 is configured to judge whether the number of nodes in the set of propagation sources is less than the preset number of propagation sources, if so, return to the second selection module, if not, take the set of propagation sources as a result and return.

An embodiment of the present invention further provides a terminal device including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the implementation is as described in the first embodiment Steps in a method for propagation source selection in complex networks.

An embodiment of the present invention further provides a storage medium, where the storage medium is a computer-readable storage medium, and a computer program is stored thereon. When the computer program is executed by a processor, the complex network as described in the first embodiment is implemented. The various steps in the method of medium propagation source selection.

The above-mentioned embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the above-mentioned embodiments have described the present invention in detail, those of ordinary skill in the art should understand that the above-mentioned embodiments can still be used for The recorded technical solutions are modified, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the present invention. within the scope of protection of the invention.

Claims (6)

1. A method for propagating source selection in a complex network, comprising:

step S1: preprocessing a complex network;

step S2: calculating the degree of each node and the betweenness of each node in the processed complex network, or calculating the importance of each node by using a webpage ranking algorithm PageRank, and taking the node with the highest influence as an initial propagation source node and adding the node into a propagation source set;

step S3: the predefined calculation formula of the comprehensive influence force is as follows:

wherein,

representing the integrated influence, S representing the set of propagation sources, w_iRepresents the influence of node i, w_ijRepresenting the overlapping influence of the node i and the node j, wherein the influence of the node i comprises the degree, the betweenness or the PageRank of the node i, and the overlapping influence of the node i and the node j comprises the degree, the betweenness or the PageRank of the edge between the node i and the node j;

calculating the comprehensive influence between each node in the processed complex network and the propagation source set according to the comprehensive influence formula, selecting the node with the maximum comprehensive influence as the propagation source node, and adding the node into the propagation source set;

step S4: calculating the influence of the newly added propagation source node and the single node of each node in the propagation source set, deleting the nodes of which the influence of the single node in the propagation source set is smaller than that of the newly added propagation source node, and updating the propagation source set;

step S5: judging whether the number of the nodes in the propagation source set is less than the preset number of the propagation sources, if so, returning to execute the step S3; if not, the propagation source set is combined as a result and returned;

wherein the calculating the single-node influence of the newly-added propagation source node and each node in the propagation source set comprises:

predefining the influence of a single node, wherein the calculation formula is as follows:

wherein, P_iRepresenting the single node influence of node i, S represents the set of propagation sources, w_iRepresents the influence of node i, w_ijRepresenting the overlapping influence of nodes i and j, wherein the influence of the node i comprises the degree, the betweenness or the PageRank of the node i, and the overlapping influence of the nodes i and j comprises the degree, the betweenness or the PageRank of the edge between the nodes i and j;

and calculating the single-node influence of the newly added propagation source node and each node in the propagation source set according to the single-node influence formula.

2. The method of propagating source selection in a complex network as in claim 1 wherein the pre-processing the complex network comprises:

and deleting isolated nodes and small node clusters in the complex network.

3. An apparatus for propagating source selection in a complex network, comprising:

the preprocessing module is used for preprocessing the complex network;

the first selection module is used for calculating the degree of each node in the processed complex network and the betweenness of each node, or calculating the importance of each node by using a webpage ranking algorithm PageRank, and taking the node with the highest influence as an initial propagation source node and adding the node into a propagation source set;

a second selection module, configured to predefine a comprehensive influence calculation formula as:

wherein,

representing the integrated influence, S representing the set of propagation sources, w_iRepresents the influence of node i, w_ijRepresenting the overlapping influence of the node i and the node j, wherein the influence of the node i comprises the degree, the betweenness or the PageRank of the node i, and the overlapping influence of the node i and the node j comprises the degree, the betweenness or the PageRank of the edge between the node i and the node j;

calculating the comprehensive influence between each node in the processed complex network and the propagation source set according to the comprehensive influence formula, selecting the node with the maximum comprehensive influence as the propagation source node, and adding the node into the propagation source set;

the screening and updating module is used for calculating the influence of the newly added propagation source node and the single node of each node in the propagation source set, deleting the node of which the influence of the single node in the propagation source set is smaller than that of the newly added propagation source node, and updating the propagation source set;

the judging module is used for judging whether the number of the nodes in the propagation source set is smaller than the preset number of the propagation sources, if so, returning to the second selecting module, and if not, combining the propagation source set into a result and returning;

wherein the calculating the single-node influence of the newly-added propagation source node and each node in the propagation source set comprises:

predefining the influence of a single node, wherein the calculation formula is as follows:

wherein, P_iRepresenting the single node influence of node i, S represents the set of propagation sources, w_iRepresents the influence of node i, w_ijRepresenting the overlapping influence of nodes i and j, wherein the influence of the node i comprises the degree, the betweenness or the PageRank of the node i, and the overlapping influence of the nodes i and j comprises the degree, the betweenness or the PageRank of the edge between the nodes i and j;

and calculating the single-node influence of the newly added propagation source node and each node in the propagation source set according to the single-node influence formula.

4. An apparatus for propagating source selection in a complex network as in claim 3 wherein the pre-processing the complex network comprises:

and deleting isolated nodes and small node clusters in the complex network.

5. A terminal device for propagating source selection in a complex network, comprising a memory, a processor and a computer program stored in said memory and executable on said processor, characterized in that said processor implements the steps of the method according to any of claims 1 to 2 when executing said computer program.

6. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 2.

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