CN109120431B - Method and device for selecting propagation source in complex network and terminal equipment - Google Patents
Method and device for selecting propagation source in complex network and terminal equipment Download PDFInfo
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Abstract
The invention is suitable for the technical field of complex network data mining, and provides a method, a device and a terminal device for selecting a propagation source in a complex network, wherein the complex network is preprocessed; calculating the influence of each node in the processed complex network to determine an initial propagation source node; respectively calculating the comprehensive influence among the initial propagation source nodes according to the overlapping influence among the initial propagation source nodes in the propagation source set, selecting the node with the maximum comprehensive influence as the propagation source node, and adding the node into the propagation source set; then updating the nodes of the propagation source according to the influence of the single node in the propagation source set; until the number of nodes in the propagation source set reaches a preset number. The invention considers the overlapping influence in the propagation source node and deletes the node with lower influence of the single node in the dense part of the propagation source node, thereby leading the distribution of the propagation source node to be more uniform and leading the information to be more efficiently propagated.
Description
Technical Field
The invention belongs to the technical field of complex network data mining, and particularly relates to a method and a device for selecting a propagation source in a complex network and terminal equipment.
Background
Many things in the real world are interrelated and can be represented in the form of a complex network, such as a social network, a treatise network, a communication network, and the like. Information is often propagated in these networks, so in order to inhibit or promote the propagation process, it is generally necessary to analyze the propagation capacity of the nodes in the network and select a propagation source node that can maximize the propagation effect.
In the process of selecting the propagation source node in the traditional method, judgment is generally carried out according to influence indexes of a single node, such as degree, betweenness, PageRank and the like, but in the method, the nodes with high influence are generally distributed very tightly, and mutual propagation among the propagation source nodes is meaningless, so that certain negative influence is caused on propagation efficiency, and the propagation efficiency of the multiple propagation source nodes selected in a complex network is not high.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for selecting a propagation source in a complex network, and a terminal device, so as to solve the problem in the prior art that a multi-propagation-source node selected in the complex network has low propagation efficiency.
A first aspect of an embodiment of the present invention provides a method for selecting a propagation source in a complex network, including:
step S1: preprocessing a complex network;
step S2: calculating the influence of each node in the processed complex network, and taking the node with the highest influence as an initial propagation source node and adding the initial propagation source node into a propagation source set;
step S3: respectively calculating the comprehensive influence among the initial propagation source nodes according to the overlapping influence among the initial propagation source nodes in the propagation source set, 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; and if not, combining the propagation source set into a result and returning.
With reference to the first aspect of the present invention, in a first implementation manner of the first aspect of the present invention, the preprocessing the complex network includes:
and deleting isolated nodes and small node clusters in the complex network.
With reference to the first aspect of the present invention, in a second implementation manner of the first aspect of the present invention, the calculating the influence of each node in the processed complex network includes:
and 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.
With reference to the first aspect of the present invention, in a third implementation manner of the first aspect of the present invention, the separately calculating a comprehensive influence between each node in the processed complex network and the propagation source set includes:
the predefined calculation formula of the comprehensive influence force is as follows:
wherein the content of the first and second substances,
representing the integrated influence, S representing the set of propagation sources, wiRepresents the influence of node i, wijRepresenting 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 node i and the node j comprises the degree, the betweenness or the PageRank of the edge between the nodes i and j;
and calculating the comprehensive influence between each node in the processed complex network and the propagation source set according to the comprehensive influence formula.
With reference to the first aspect of the present invention, in a fourth implementation manner of the first aspect of the present invention, the calculating a single-node influence of the newly-added propagation source node and each node in the propagation source set includes:
predefining the influence of a single node, wherein the calculation formula is as follows:
wherein, PiRepresenting the single node influence of node i, S represents the set of propagation sources, wiRepresents the influence of node i, wijRepresenting 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.
A second aspect of an embodiment of the present invention provides an apparatus for propagating source selection in a complex network, including:
the preprocessing module is used for preprocessing the complex network;
the first selection module is used for calculating the influence of each node in the processed complex network, 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 calculate a comprehensive influence between each node in the processed complex network and the propagation source set, and select a node with the highest comprehensive influence as a propagation source node and add the node to 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;
and the judging module is used for 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 the second selecting module, and if not, combining the propagation source set into a result and returning.
With reference to the second aspect of the present invention, in a first implementation manner of the second aspect of the present invention, the preprocessing the complex network includes:
and deleting isolated nodes and small node clusters in the complex network.
With reference to the second aspect of the present invention, in a second implementation manner of the second aspect of the present invention, the calculating the influence of each node in the processed complex network includes:
and 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.
A third aspect of the embodiments of the present invention provides a terminal device for propagating source selection in a complex network, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method provided in the first aspect when executing the computer program.
A fourth aspect of embodiments of the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method as provided in the first aspect above.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
the method comprises the steps of firstly preprocessing a complex network; then calculating the influence of each node in the complex network after complex processing to determine an initial propagation source node; respectively calculating the comprehensive influence among the initial propagation source nodes according to the overlapping influence among the initial propagation source nodes in the propagation source set, selecting the node with the maximum comprehensive influence as the propagation source node, and adding the node into the propagation source set; then deleting the nodes of which the influence of the single nodes in the propagation source set is smaller than that of the nodes newly added into the propagation source; and judging whether the number of the nodes in the propagation source set reaches a preset number, and if not, continuing to calculate the comprehensive influence to add new nodes into the propagation source set until the preset number is reached. Compared with the prior art, the invention deletes the nodes with lower influence of the single nodes in the dense positions of the propagation source nodes by considering the overlapping influence in the propagation source nodes, thereby ensuring that the propagation source nodes are distributed more uniformly and the information can be propagated more efficiently.
Drawings
Fig. 1 is a schematic flow chart illustrating an implementation of a method for propagating source selection in a complex network according to an embodiment of the present invention;
fig. 2 is a schematic diagram illustrating an implementation effect of a method for propagating source selection in a complex network according to a second embodiment of the present invention;
fig. 3 is a schematic diagram of a first metric of a method for selecting a propagation source in a complex network according to a third embodiment of the present invention;
fig. 4 is a schematic diagram of a second metric related to a method for selecting a propagation source in a complex network according to a third embodiment of the present invention;
fig. 5 is a schematic diagram of a third metric related to a method for selecting a propagation source in a complex network according to a third embodiment of the present invention;
fig. 6 is a schematic diagram of a fourth metric related to a method for selecting a propagation source in a complex network according to a third embodiment of the present invention;
fig. 7 is a schematic structural diagram of an apparatus for propagating source selection in a complex network according to a fourth embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Suffixes such as "module", "part", or "unit" used to denote elements are used herein only for the convenience of description of the present invention, and have no specific meaning in themselves. Thus, "module" and "component" may be used in a mixture.
In the following description, the serial numbers of the embodiments of the invention are merely for description and do not represent the merits of the embodiments.
Example one
As shown in fig. 1, an embodiment of the present invention provides a method for selecting a propagation source in a complex network, where the method includes:
step S1: and preprocessing the complex network.
In step S1, any network can produce and distribute information, and all information produced and distributed by the network can flow into the network in a non-linear manner.
In the embodiment of the present invention, a complex network a ═ { a ═ is definedij}N×N，aijWhere a denotes an adjacency matrix of the complex network, N is a complex network scale size, i and j denote two nodes, and a is a when i and j have an edge ij1 otherwise aij0. The number of propagation sources is defined as L.
In one embodiment, the preprocessing the complex network includes: and deleting isolated nodes and small node clusters in the complex network.
In a specific application, the isolated nodes in the complex network can be obtained by any method capable of obtaining the isolated nodes, for example, by calculating an equation satisfied by the isolated nodes; the small node clusters in the complex network can be obtained by any method capable of obtaining the small node clusters, for example, by calculating the distance between the nodes; after the isolated nodes and the small node clusters in the complex network are deleted, the maximum connected subgraph in the network can be reserved, and 4-core data of the network can be extracted. The 4-core data includes, but is not limited to, a number of 4-core network nodes and a number of 4-core network edges.
Step S2: and calculating the influence of each node in the processed complex network, and taking the node with the highest influence as an initial propagation source node and adding the initial propagation source node into a propagation source set.
In step S2, the importance of the node and the importance of the edge are calculated based on the degree, the betweenness, the PageRank, and the like, respectively, based on the importance index of the node, and the propagation source is initialized with the node having the highest importance.
In one embodiment, the calculating the influence of each node in the processed complex network includes: and 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.
In specific applications, Degree Centricity (DC), abbreviated as Degree, is one of the simplest measures. Assuming that the number of nodes in a network is N, the maximum possible value of a node is N-1, and one degree is defined as KiThe degree centrality of the node of (a) is:
wherein, if in the directed network, there are the scores of in-degree and out-degree, and at this time, the centrality is calculated respectively. The centrality only considers the surrounding nodes of one node, only the locality feature.
In a specific application, Betweenness Center (BC), abbreviated as Betweenness, is divided into node Betweenness center and edge Betweenness center, and node Betweenness is defined by a formula:
wherein, gstThe number of shortest paths from node s to node t,
is g from node s to node tstOf shortest paths through node iNumber of the cells.
The edge betweenness is defined by the formula:
In a specific application, a webpage ranking algorithm (PageRank, PR) is widely applied to node importance ranking. The calculation formula is as follows:
step S3: and respectively calculating the comprehensive influence among the initial propagation source nodes according to the overlapping influence among the initial propagation source nodes in the propagation source set, selecting the node with the maximum comprehensive influence as the propagation source node, and adding the node into the propagation source set.
In the above steps S2 and S3, the node with the highest influence is taken as the initial propagation source node and added to the propagation source set, and the node with the highest comprehensive influence is taken as the propagation source node and added to the propagation source set, so that the data initialization of the propagation source set is completed.
In one embodiment, the separately calculating the composite influence between each node in the processed complex network and the set of propagation sources includes:
the comprehensive influence is predefined, and the calculation formula is as follows:
wherein the content of the first and second substances,
representing the integrated influence, S representing the set of propagation sources, wiRepresents the influence of node i, wijRepresenting 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 node i and the node j comprises the degree, the betweenness or the PageRank of the edge between the nodes i and j;
and calculating the comprehensive influence between each node in the processed complex network and the propagation source set according to the comprehensive influence formula.
In a particular application, in the degree-based approach, wiRepresenting degrees of node i, w if there is an edge between i, j ij1, otherwise w ij0; in the betweenness-based approach, wiDenotes the betweenness of the node i, wijRepresents the betweenness of the edges; based on the PageRank method, the iteration number is set to be 5, and w is calculated firstlyiInitialize PRi(0) 1 and PRj，j≠i(0) 0, during each iteration, the PageRank value corresponding to each node is calculated according to the PageRank formula and PR is reinitializedi(k) 1, 2, 3 and 4. Final wiWhich is the sum of the PageRank values of the non-propagating source nodes. When an edge exists between the nodes i, j, then w is calculatedijInitialize PRi(0)＝PRj(0) 1 and PRm，m≠i，j(0) At each iteration, the PageRank value for each node is calculated and PR is reinitialized as well, 0i(k)＝PRj(k) 1, k 1, 2, 3, 4, and finally wijWhich is the sum of the PageRank values of the non-propagating source nodes.
Step S4: and 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.
In the above step S4, each new propagation source node is added, and the magnitude of the single influence of each propagation source node is calculated and compared, and if there is a node in the propagation source set whose influence is smaller than that of the newly added node, the node is deleted.
In one embodiment, said calculating the single-node influence of said newly-joined propagation source node with each node in said set of propagation sources comprises:
predefining the influence of a single node, wherein the calculation formula is as follows:
wherein, PiRepresenting the single node influence of node i, S represents the set of propagation sources, wiRepresents the influence of node i, wijRepresenting 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.
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; and if not, combining the propagation source set into a result and returning.
In step S5, when the number of propagation sources reaches a set value, for example, L, the selected propagation source node is associated with the index of the original network and returned. As the number of the propagation source nodes increases, the overlapping influence becomes higher, and the information cannot be propagated out quickly, so that the overlapping influence between the nodes needs to be reduced.
The method for selecting the propagation source in the complex network provided by the embodiment of the invention comprises the steps of preprocessing the complex network; then calculating the influence of each node in the complex network after complex processing to determine an initial propagation source node; respectively calculating the comprehensive influence among the initial propagation source nodes according to the overlapping influence among the initial propagation source nodes in the propagation source set, selecting the node with the maximum comprehensive influence as the propagation source node, and adding the node into the propagation source set; then deleting the nodes of which the influence of the single nodes in the propagation source set is smaller than that of the nodes newly added into the propagation source; and judging whether the number of the nodes in the propagation source set reaches a preset number, and if not, continuing to calculate the comprehensive influence to add new nodes into the propagation source set until the preset number is reached. Compared with the prior art, the invention deletes the nodes with lower influence of the single nodes in the dense positions of the propagation source nodes by considering the overlapping influence in the propagation source nodes, thereby ensuring that the propagation source nodes are distributed more uniformly and the information can be propagated more efficiently.
Example two
As shown in fig. 2, the embodiment of the present invention further provides an effect description of the method for propagating source selection in a complex network in steps S1 to S5 in the first embodiment of the present invention in practical application. Nodes 1 to 7 and 12, which are marked in black, are nodes in a large number of degrees, while nodes 8 to 11 and 13 to 15, which are marked in gray, are nodes in a small number of degrees. Assuming that the number of propagation sources is 4, that is, 4 nodes are selected as propagation sources, in the conventional method based on degrees, the nodes 1, 2, 3 and 4 should be preferentially selected, and obviously, information cannot be quickly propagated because the nodes with small degrees are slightly far away from the propagation source nodes, and the internal connections between the propagation source nodes are tight. When the overlapping influence among the propagation source nodes is reduced, namely the nodes 1, 4, 7 and 12 with large degrees and sparse distribution are selected, information can be spread out in the whole network quickly, and the overlapping influence of the nodes in the propagation source set is low at the moment.
EXAMPLE III
The embodiment of the invention aims at the method for propagating source selection in the complex network provided by the first embodiment and uses data to illustrate the beneficial effect in the practical application of the method.
In the embodiment of the present invention, four real networks are selected: facebook, CA-HepPh, Hamster, as-caida experiments were performed for spreading source selection in complex networks according to the method in example one.
Firstly, considering the network structure as being unwarranted and undirected, when preprocessing is carried out, deleting isolated nodes and small node clusters, only reserving the maximum connected subgraph, and extracting 4-core data of the network, wherein because of the self-similarity of the network, the sub-network has similar structural characteristics with the original network, and the specific characteristics of the network are as follows:
in the embodiment of the present invention, several metrics are proposed to prove the beneficial effect of the method for propagating source selection in a complex network provided in the first embodiment.
The first metric is the coverage of information propagation in the network, which embodies the propagation capability of the propagation source node:
wherein N islAnd NRRespectively representing the number of infected nodes and immune nodes when the propagation reaches a steady state, and N is the total number of the nodes.
As shown in fig. 3, the first metric provided 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 the first embodiment is much higher than the propagation range corresponding to the first metric in the conventional method, especially in (a) facebook and (c) the pamsrer-based method, the improvement range is significantly improved, and the final propagation range is almost in the entire network. Since the number of the source nodes is small at the beginning of propagation, the corresponding overlapping influence between the source nodes is relatively small, and the improved method effect is not obvious at the moment; however, as the number of the propagation source nodes increases, the overlapping influence is higher, and the improvement effect of the improved method proposed in the first embodiment also increases significantly.
In practical application, the number of friends shared by two people far away is small, and two people close to each other often have more friends. The average distance between the propagation source nodes most easily reflects the overlapping influence between the nodes. Therefore, a second metric d is provided, which embodies the average distance between the propagation source nodes:
where L represents the number of propagation source nodes, dijRepresenting the distance between nodes i and j.
As shown in fig. 4, the second metric proposed by 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, which exactly reflects that the overlapping influence of the propagation source nodes in the network is reduced. The improvement of the betweenness method corresponding to the (c) hamster network is not obvious, and the similarity of the propagation capacity of the (c) hamster network in fig. 3 is also reflected.
In practical applications, the overlapping impact can be characterized by the similarity of the propagation source nodes, in addition to the average distance. Generally speaking, a node with low similarity usually means that the overlapping influence between nodes is low, so a third metric s is provided to reflect the similarity of the propagation source node:
wherein S isiAnd SjRepresenting the set of neighbor nodes for node i and node j, respectively.
As shown in fig. 5, a third metric 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 similarities between the nodes in the propagation source set, which are obtained by the excessive method and the method for improving PageRank, are both reduced. But the similarity between nodes of the method improved by betweenness is very low, and compared with the traditional method, the method has no obvious change.
It can be known from fig. 4 and fig. 5 that the propagation sources selected by the two betweenness methods are different, so that the similarity is not a good indicator for judging the overlapping influence of the betweenness methods, and the average distance can better reflect the overlapping influence between the nodes.
Finally, the embodiment of the present invention provides a fourth metric σ, and the diversity of the propagation source node selection is reflected by the intersection of the propagation source node set selected by the method for selecting a propagation source in a complex network and the conventional method provided in the first embodiment:
wherein S is1Set of propagation source nodes selected for the method of propagation source selection in a complex network provided in the first embodiment, S2A set of propagation source nodes selected for a conventional approach.
As shown in fig. 6, the intersection between the conventional method and the set of propagation source node selected by the method for propagation source selection in a complex network provided in the first embodiment is reflected. For example, (a) in the PageRank method corresponding to the facebook network, σ <0.4, which indicates the diversity of the selection of the propagation source node. In the (c) betweenness method corresponding to the hamster network, σ >0.8, which means that the propagation source nodes selected by the two methods are almost similar, which just explains that the two betweenness methods in fig. 4 have similar propagation capability.
EXAMPLE III
As shown in fig. 7, an apparatus 30 for propagating source selection in a complex network according to an embodiment of the present invention includes:
and the preprocessing module 71 is used for preprocessing the complex network.
In one embodiment, the preprocessing the complex network includes: and deleting isolated nodes and small node clusters in the complex network.
And a first selecting module 72, configured to calculate an influence of each node in the processed complex network, and take the node with the highest influence as an initial propagation source node and add the initial propagation source node into the propagation source set.
In one embodiment, the calculating the influence of each node in the processed complex network includes: and 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.
The second selecting module 73 calculates 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, selects the node with the maximum comprehensive influence as the propagation source node, and adds the node into the propagation source set.
And a screening and updating module 74, configured to calculate a single-node influence of the newly added propagation source node and each node in the propagation source set, delete a node in the propagation source set whose single-node influence is smaller than that of the newly added propagation source node, and update the propagation source set.
A judging module 75, configured to judge whether the number of nodes in the propagation source set is smaller than a preset number of propagation sources, if so, return to the second selecting module, and if not, combine the propagation source set as a result and return.
The embodiment of the present invention further provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where when the processor executes the computer program, each step in the method for selecting a propagation source in a complex network as described in the first embodiment is implemented.
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 on the storage medium, where the computer program, when executed by a processor, implements each step in the method for propagating source selection in a complex network according to the first embodiment.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the foregoing embodiments illustrate the present invention in detail, those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present 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 the content of the first and second substances,
representing the integrated influence, S representing the set of propagation sources, wiRepresents the influence of node i, wijRepresenting 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, PiRepresenting the single node influence of node i, S represents the set of propagation sources, wiRepresents the influence of node i, wijRepresenting 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 the content of the first and second substances,
representing the integrated influence, S representing the set of propagation sources, wiRepresents the influence of node i, wijRepresenting 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, PiRepresenting the single node influence of node i, S represents the set of propagation sources, wiRepresents the influence of node i, wijRepresenting 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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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104102703A (en) * | 2014-07-08 | 2014-10-15 | 华中师范大学 | Method for estimating node transmission capacity in complex network |
| CN106951524A (en) * | 2017-03-21 | 2017-07-14 | 哈尔滨工程大学 | Overlapping community discovery method based on node influence power |
| CN107682200A (en) * | 2017-10-26 | 2018-02-09 | 杭州师范大学 | A kind of method of the transmission on Internet source positioning based on finite observation |
-
2018
- 2018-07-12 CN CN201810761173.3A patent/CN109120431B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104102703A (en) * | 2014-07-08 | 2014-10-15 | 华中师范大学 | Method for estimating node transmission capacity in complex network |
| CN106951524A (en) * | 2017-03-21 | 2017-07-14 | 哈尔滨工程大学 | Overlapping community discovery method based on node influence power |
| CN107682200A (en) * | 2017-10-26 | 2018-02-09 | 杭州师范大学 | A kind of method of the transmission on Internet source positioning based on finite observation |
Non-Patent Citations (1)
| Title |
|---|
| 大规模网络中多传播源的重叠影响力问题研究;周明洋 等;《中国科学技术大学学报》;20160131;第46卷(第1期);第28-34页，第2节 * |
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