CN110247805B - Method and device for identifying propagation key nodes based on K-shell decomposition - Google Patents
Method and device for identifying propagation key nodes based on K-shell decomposition Download PDFInfo
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Abstract
The invention discloses a method and a device for identifying propagation key nodes based on K shell decomposition, wherein the method comprises the following steps: establishing a propagation network by collecting message forwarding of a social platform, and determining an individual forwarding a message in the propagation network as a node; obtaining the number of connecting edges corresponding to each node according to the friend list data; calculating K shell indexes of each node according to the node degree; calculating the shortest distance between each pair of nodes in the propagation network, wherein the shortest distance between the nodes is used for representing the propagation position of the individual in the social network; and calculating the ranking corresponding score of each node according to the shortest distance between the K-shell index and the nodes, and further obtaining a key propagator in the propagation network. The method and the device can more accurately position the node in the network, accurately excavate key propagators in the social network, and reduce the misjudgment rate.
Description
Technical Field
The invention relates to the field of network information mining, in particular to a method and a device for identifying propagation key nodes based on K-shell decomposition.
Background
Today in the network society, the message spreading is very rapid, and the key nodes in the spreading are special individuals which can affect the structure and function of the network to a greater extent, while the key nodes in the rumor spreading network refer to nodes which can maximally accelerate the rumor spreading. For example, microbolog V can accelerate rumor spread. Therefore, we need to accurately discover key propagators in the rumor propagation network from a large number of users, which helps us to better control the rumor propagation.
At present, most of rumor propagation adopts a mode of manual deletion, and some people adopt a node ordering method for a rumor propagation network, but only adopt a single method to determine the position or influence degree of a node, but the node at the center of the network may also appear at the edge of the network, the node position with high influence degree may also be biased, and the accuracy of identifying key propagators of the rumor propagation network is low.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects of the prior art, the invention provides a method for identifying propagation key nodes based on K-shell decomposition, which can solve the problem of error evaluation of high-K-shell nodes at the edge of a network and more accurately mine key propagators of a propagation network.
The technical scheme is as follows: the invention discloses a method for identifying propagation key nodes based on K-shell decomposition, which comprises the following steps:
establishing a transmission network by collecting message transmission of a social platform, determining a node set according to an individual transmitting a message in the transmission network, and collecting friend list data corresponding to each node in the node set;
obtaining the number of the directly connected edges corresponding to each node according to the friend list data; if the individuals are in a friend relationship, a direct connection edge exists between the two corresponding nodes;
determining the degree of each node in the propagation network according to the number of the directly connected edges of each node, and calculating a K-shell index of each node according to the degree of each node, wherein the K-shell index is used for representing the influence of individuals in the propagation network;
and calculating the shortest distance between each pair of nodes in the propagation network by using a Floyd algorithm, and calculating the ranking corresponding score of each node according to the K shell index and the shortest distance so as to obtain the propagation key nodes in the propagation network.
Further, comprising:
the determining the degree of each node in the propagation network according to the number of the directly connected edges of each node specifically includes: the degree of a node in the propagation network is equal to the number of directly successive edges of the node.
Further, comprising:
the K shell index of each node is calculated according to the node degree, and the method specifically comprises the following steps:
obtaining degrees of each node in a node set S in the propagation network, wherein the node set S is { S }1,s2,...,snN is the total number of nodes;
traversing the node set S, searching all nodes with the degree of 1, deleting the corresponding nodes and the straight edges of the nodes, and storing the corresponding nodes into the set S1Said set S1K-shell index of each node in the tree
Traversing the node set S, searching all nodes with the degree of 2, and deleting the corresponding nodes and the nodesThe connected edges store the corresponding nodes in a set S2Said set S2K-shell index of each node in the tree
Repeatedly traversing the node set S, adding 1 to the searched degree each time until all nodes corresponding to the maximum degree n in the set are searched, deleting the corresponding nodes and the edges connected with the nodes, and storing the corresponding nodes into the set SnSaid set SnK-shell index of each node in the tree
Further, comprising:
the calculating the shortest distance between each pair of nodes in the propagation network by using the Floyd algorithm comprises the following steps:
acquiring a network adjacency matrix G corresponding to the propagation network;
initializing a distance matrix D according to an adjacency matrix G, and if a path from a node i to a node j can be reached, setting the distance matrix D [ i ] [ j ] ═ D, wherein D represents the length of the path; otherwise, D [ i ] [ j ] becomesinfinite, wherein i is more than or equal to 1 and less than j and less than or equal to n;
defining a matrix L to record the information of the inserted node, wherein L [ i ] [ j ] represents the node which needs to pass from the node i to the node j, and initializing L [ i ] [ j ] ═ j; inserting other nodes into the paths from i to j, and comparing the distance from i to j after the nodes are inserted with the original distance, wherein the distance is expressed as: d [ i ] [ j ] ═ min (D [ i ] [ j ], D [ i ] [ k ] + D [ k ] [ j ]), and if the value of D [ i ] [ j ] becomes smaller, L [ i ] [ j ] ═ k; and repeating the operation until the D is not updated any more, and outputting the distance matrix D.
Further, comprising:
the formula for calculating the ranking corresponding score of each node is as follows:
wherein, KS(t) is the t-th sectionCorresponding K shell index, di,tRepresenting the shortest distance between nodes i and t.
An apparatus for identifying propagation critical nodes based on K-shell decomposition, comprising:
the system comprises an acquisition module, a transmission module and a processing module, wherein the acquisition module is used for establishing a transmission network by acquiring message transmission of a social platform, determining a node set according to an individual transmitting a message in the transmission network, and acquiring friend list data corresponding to each node in the node set;
the direct connection edge calculation module is used for obtaining the number of the direct connection edges corresponding to each node according to the friend list data; if the individuals are in a friend relationship, a direct connection edge exists between the two corresponding nodes;
the K-shell index calculation module is used for determining the degree of each node in the propagation network according to the number of the directly connected edges of each node, and calculating the K-shell index of each node according to the degree of each node, wherein the K-shell index is used for representing the influence of individuals in the propagation network;
and the score calculation module is used for calculating the shortest distance between each pair of nodes in the propagation network by adopting a Floyd algorithm, and calculating the ranking corresponding score of each node according to the K shell index and the shortest distance so as to obtain the propagation key nodes in the propagation network.
Further, comprising:
in the K-shell index calculation module, the degree of the nodes in the propagation network is equal to the number of the directly connected edges of the nodes.
Further, comprising:
the K-shell index calculation module further includes:
obtaining degrees of each node in a node set S in the propagation network, wherein the node set S is { S }1,s2,...,snN is the total number of nodes;
traversing the node set S, searching all nodes with the degree of 1, deleting the corresponding nodes and the straight edges of the nodes, and storing the corresponding nodes into the set S1Said set S1K-shell index of each node in the tree
Traversing the node set S, searching all nodes with the degree of 2, deleting the corresponding nodes and the edges connected with the nodes, and storing the corresponding nodes into the set S2Said set S2K-shell index of each node in the tree
Repeatedly traversing the node set S, adding 1 to the searched degree each time until all nodes corresponding to the maximum degree n in the set are searched, deleting the corresponding nodes and the edges connected with the nodes, and storing the corresponding nodes into the set SnSaid set SnK-shell index of each node in the tree
Further, comprising:
the calculating the shortest distance between each pair of nodes in the propagation network by using the Floyd algorithm further comprises:
acquiring a network adjacency matrix G corresponding to the propagation network;
initializing a distance matrix D according to an adjacency matrix G, and if a path from a node i to a node j can be reached, setting the distance matrix D [ i ] [ j ] ═ D, wherein D represents the length of the path; otherwise, D [ i ] [ j ] becomesinfinite, wherein i is more than or equal to 1 and less than j and less than or equal to n;
defining a matrix L to record the information of the inserted node, wherein L [ i ] [ j ] represents the node which needs to pass from the node i to the node j, and initializing L [ i ] [ j ] ═ j; inserting other nodes into the paths from i to j, and comparing the distance from i to j after the nodes are inserted with the original distance, wherein the distance is expressed as: d [ i ] [ j ] ═ min (D [ i ] [ j ], D [ i ] [ k ] + D [ k ] [ j ]), and if the value of D [ i ] [ j ] becomes smaller, L [ i ] [ j ] ═ k; and repeating the operation until the D is not updated any more, and outputting the distance matrix D.
Further, comprising:
in the score calculation module, a formula for calculating the ranking corresponding score of each node is as follows:
wherein, KS(t) is the K shell index corresponding to the t-th node, di,tRepresenting the shortest distance between nodes i and t.
Has the advantages that: the invention improves the common indexes of the network nodes to obtain the ranking score which can comprehensively reflect the roles of the key propagation nodes in the propagation network, can more accurately position the nodes in the network, accurately excavates key propagators in the social network and reduces the misjudgment rate; in addition, the Floyd algorithm is adopted to calculate the distance between the nodes, compared with other distance algorithms, the time complexity is low, the time cost is lower when the method is suitable for a complex social network, and the identification efficiency is improved.
Drawings
FIG. 1 is a flow chart of an identification method in an embodiment of the present invention;
FIG. 2 is a network node connection diagram according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating a structure of an identification device according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device in an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In addition to the number of individuals that can be directly influenced, whether the position of the node where the node is located is sufficiently central determines the influence of the node on the propagation, so the node ranking method needs to consider the influence of the node itself (the number of individuals with whom the node is closely related in the social network) and the position of the node in the network (the position of the node where the node is located in the social network). The current classical node sequencing methods do not consider the roles of the two aspects at the same time, so that the roles of the nodes in the network cannot be comprehensively evaluated by applying the existing methods.
The k-shell decomposition method is widely used due to its low computational complexity and excellent recognition effect. Often, a node with a larger k shell is located in the center of the network, but may also appear at the edge of the network, and the k shell decomposition method still considers the node to be important, but the fact is not.
In order to solve the problems, the invention designs a new node sequencing method by integrating node path information into a k-shell decomposition method to improve the identification accuracy, corrects the error evaluation of the k-shell decomposition method on high k-shell nodes at the edge of the network, more accurately excavates key propagators of the network to be propagated, and has great application value.
Referring to fig. 1, the present invention provides a method for identifying propagation key nodes based on K-shell decomposition, which specifically includes:
s110, establishing a propagation network by collecting message forwarding of a social platform, determining a node set according to individuals forwarding messages in the propagation network, and collecting friend list data corresponding to each node in the node set, wherein the node set S is { S ═ S1,s2,...,snN is the total number of nodes.
In the embodiment of the invention, a specific network is not limited, the whole chain for forwarding a certain message on a microblog or other social platform can be collected, and a propagation network is established according to the whole chat.
S120, obtaining the number of connecting edges corresponding to each node according to the friend list data; if the individuals are in a friend relationship, a direct connection edge exists between the two corresponding nodes;
specifically, the method does not limit specific social software, if the direct friend relationship exists in the friend list of the individual, the friend list relationship in the corresponding database in the social software is acquired, if the friend relationship exists between the two individuals, a direct connection edge exists, if the friend relationship does not exist between the two individuals, the direct connection edge does not exist, and the individuals which are not friends are mutually influenced by the individuals which are friends at the same time.
S130, determining the degree of each node in the propagation network according to the number of the direct connection edges of each node, and calculating the K-shell index of each node according to the degree of the node, wherein the K-shell index is used for representing the direct influence of individuals in the propagation network.
The number of the directly connected edges corresponding to each node is the degree corresponding to the node and is represented as K (i), wherein i is the network node number, i is more than or equal to 1 and less than or equal to n, n is the total number of the nodes in the propagation network, and meanwhile, the node with the directly connected edge is a neighbor node corresponding to the node.
The calculating the K-shell index of each node according to the degree of the node specifically includes:
inputting: the degree of each node in the node set S in the propagation network;
the method comprises the following steps: s1 traversing the node set S, searching all nodes with degree of 1, deleting the corresponding nodes and the edges connected with the nodes, and storing the corresponding nodes in the set S1Said set S1K-shell index of each node in the tree
S2 traversing the node set S, searching all nodes with degree of 2, deleting the corresponding nodes and the edges connected with the nodes, and storing the corresponding nodes in the set S2Said set S2K-shell index of each node in the tree
S3 iterates through the set of nodes S, and the degree of search is incremented by 1 each time,until all nodes corresponding to the maximum degree n in the set are found, the corresponding nodes and the edges connected with the nodes are deleted, and the corresponding nodes are stored in a set SnSaid set SnK-shell index of each node in the tree
And (3) outputting: set of nodes S1,S2,...,SnAnd K shell index of corresponding node
S140, calculating the shortest distance between each pair of nodes in the propagation network by using a Floyd algorithm, wherein the shortest distance between the nodes is used for correcting the K-shell index of each node.
For example, a high-k shell node appearing at an edge of the network, whose influence is overestimated due to its great distance from other nodes of the network, should be given a lower weight, and we can describe this weight by the reciprocal of the distance between nodes, because the longer the distance, the smaller the reciprocal of the distance, the less important the node is.
The calculating the shortest distance between each pair of nodes in the propagation network by using the Floyd algorithm comprises the following steps:
inputting: a network adjacency matrix G corresponding to the propagation network;
the method comprises the following steps: (1) initializing a distance matrix D according to an adjacency matrix G, and if a path from a node i to a node j can be reached, setting the distance matrix D [ i ] [ j ] ═ D, wherein D represents the length of the path; otherwise, D [ i ] [ j ] becomesinfinite, wherein i is more than or equal to 1 and less than j and less than or equal to n;
(2) defining a matrix L to record the information of the inserted node, wherein L [ i ] [ j ] represents the node which needs to pass from the node i to the node j, and initializing L [ i ] [ j ] ═ j; inserting other nodes into the path of i → j, comparing the distance of i → j after inserting the nodes with the original distance, namely D [ i ] [ j ] min (D [ i ] [ j ], D [ i ] [ k ] + D [ k ] [ j ]), and if the value of D [ i ] [ j ] is reduced, L [ i ] [ j ] ═ k; repeating the above operations until D is not updated any more;
and (3) outputting: a distance matrix D between nodes in the network.
Further, comprising:
the ranking score formula of each node is as follows:
wherein, KS(t) is the K shell index corresponding to the t-th node, di,tRepresents the shortest distance between nodes i and t
S150, calculating ranking corresponding scores of all the nodes according to the K shell indexes and the shortest distance between the nodes, and further obtaining key propagators in the propagation network.
It should be noted that the method flowchart in the embodiment of the present invention is to more clearly illustrate the technical solution in the embodiment of the present invention, and is not limited to the technical solution provided in the embodiment of the present invention, and the embodiment of the present invention is also not limited to the application of a social network, and for other system structures and business applications, the technical solution provided in the embodiment of the present invention is also applicable to similar problems.
The following detailed description of the embodiments of the present invention will be made with reference to the example network of fig. 2.
Step 1: and calculating K shell indexes of all nodes. The nodes (7, 8, 10) with the degree of 1 in the network and the connecting edges thereof are removed, and the nodes with the degree of 1 in the network are not available, so that the k-shell index of the nodes 7, 8, 10 is 1, and the network only comprises seven nodes (1, 2, 3, 4, 5, 6, 9). And then removing nodes (5, 6, 9) and connecting edges thereof with the degree of 2 in the network, wherein the nodes (3, 4) with the degree of 2 still exist in the network, removing the nodes 3, 4 and connecting edges thereof, and remaining nodes (1, 2) with the degree of less than or equal to 2 still exist in the network, so that the nodes 1, 2 are removed, no nodes exist in the network at this time, and the k shell indexes of the nodes 1, 2, 3, 4, 5, 6, 9 are all 2. The K shell indices for all nodes are given in table 1:
TABLE 1 k-Shell indices for each node
Step 2: calculating the shortest distance d between each pair of nodes in the networkij。
The input is the corresponding network adjacency matrix, and the output is the distance matrix, and the corresponding adjacency matrix in this embodiment is represented as:
0 1 1 1 0 0 0 0 0 0
1 0 1 1 0 0 0 0 1 0
1 1 0 0 0 0 0 0 1 1
1 1 0 0 1 1 0 0 0 0
0 0 0 1 0 1 1 1 0 0
0 0 0 1 1 0 0 0 0 0
0 0 0 0 1 0 0 0 0 0
0 0 0 0 1 0 0 0 0 0
0 1 1 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0
taking node 1 as an example, since there are straight edges connecting with nodes 2, 3, and 4, the corresponding position is 1, and since there are no straight edges connecting with the remaining six nodes, the corresponding position is 0.
See table 2: the rows and columns in the table are the corresponding numbers of the nodes, and the output values correspond to the shortest distance matrix between the nodes, taking the node pair (1, 6) as an example, because the node 1 and the node 6 are not directly adjacent (a)1,60), the initial distance is set to infinity; then node 2 is inserted between nodes 1, 6, node 1 and node 6 cannot reach each other; inserting node 3, node 1 and node 6 still cannot reach each other; inserting node 4, node 1 and node 6 can reach each other, so the shortest distance between them is updated to 2; nodes 5, 6, 7, 8, 9 and 10 are added in sequence, and the shortest distance between the node 1 and the node 6 is updated, so d1,6＝2。
TABLE 2 shortest distance between pairs of nodes
And step 3: and calculating the ranking score of each node, wherein the higher the ranking score is, the more important the node is.
Taking node 1 as an example:
the scores for all nodes are shown in table 3:
TABLE 3 score of each node
From the scores of table 3, a final node importance ranking can be obtained, see table 4:
TABLE 4 importance ranking of nodes
As can be seen from table 4, node 4 is the most important. It can be readily seen from fig. 1 that the node 4 is connected to two small communities on the left and right, and the node 4 in the "core network location" is indeed the most important node. Reflecting to the real propagation network, the node 4 has the best message propagation position and can not be used as a medium for communication between two groups. Compared with other nodes, such as node 5, a longer message propagation path is needed to reach the right group. If node 4 acts as a message spreader, more specifically, rumors will spread rapidly across the two groups around it, speeding up rumor propagation.
Based on the above embodiment, referring to fig. 3, in an embodiment of the present invention, an apparatus for identifying propagation key nodes based on K-shell decomposition includes:
the acquisition module 21 establishes a propagation network by acquiring message forwarding of the social platform, numbers each node with an individual forwarding a message in the propagation network as a node to obtain a node set S ═ S1,s2,...,snAnd collecting each node in the node setFriend list data corresponding to each node, wherein n is the total number of the nodes;
a direct connecting edge calculating module 22, configured to obtain the number of connecting edges corresponding to each node according to the buddy list data; if the individuals are in a friend relationship, a direct connection edge exists between the two corresponding nodes;
a K-shell index calculation module 23, configured to determine degrees of each node in the propagation network according to the number of directly-connected edges of each node, and calculate a K-shell index of each node according to the degrees of the nodes, where the K-shell index is used to represent a direct influence of an individual in the propagation network;
the shortest distance calculation module 24 is configured to calculate a shortest distance between each pair of nodes in the propagation network by using a Floyd algorithm, where the shortest distance between the nodes is used to represent a propagation position of an individual in a social network;
and the score calculating module 25 is configured to calculate a ranking corresponding score of each node according to the shortest distance between the K-shell index and the node, so as to obtain a key propagator in the propagation network.
Further, comprising:
in the K-shell index calculation module 23, the degree of a node in the propagation network is equal to the number of directly connected edges of the node.
Further, comprising:
the K-shell index calculating module 23 further includes:
an input unit, configured to input degrees of each node in a node set S in the propagation network;
a traversal unit for first traversing the node set S, searching all nodes with the degree of 1, deleting the corresponding nodes and the edges connected with the nodes, and storing the corresponding nodes into the set S1Said set S1K-shell index of each node in the tree
Secondly, traversing the node set S, searching all nodes with the degree of 2, deleting the corresponding nodes and the edges connected with the nodes,storing the corresponding node to a set S2Said set S2K-shell index of each node in the tree
And finally, repeatedly traversing the node set S, adding 1 to the searched degree each time until all nodes corresponding to the maximum degree n in the set are searched, deleting the corresponding nodes and the edges connected with the nodes, and storing the corresponding nodes into the set SnSaid set SnK shell index K of each node insn(m)＝n，m∈Sn；
Output unit, output node set S1,S2,...,SnAnd K shell index of corresponding node
Further, comprising:
the shortest distance calculating module 24 further includes:
an input unit that inputs a network adjacency matrix G corresponding to the propagation network;
a calculating unit, preferably, initializing a distance matrix D according to the adjacency matrix G, and if a path from the node i to the node j is reachable, the distance matrix D [ i ] [ j ] ═ D, and D represents the length of the path; otherwise, D [ i ] [ j ] becomesinfinite, wherein i is more than or equal to 1 and less than j and less than or equal to n;
secondly, defining a matrix L for recording the information of the inserted node, wherein L [ i ] [ j ] represents the node which needs to pass from the node i to the node j, and initializing L [ i ] [ j ] ═ j; inserting other nodes into the path of i → j, comparing the distance of i → j after inserting the nodes with the original distance, namely D [ i ] [ j ] min (D [ i ] [ j ], D [ i ] [ k ] + D [ k ] [ j ]), and if the value of D [ i ] [ j ] is reduced, L [ i ] [ j ] ═ k; repeating the above operations until D is not updated any more;
and the output unit is used for outputting the distance matrix D between the nodes in the network.
Further, comprising:
in the score calculation module 25, the ranking score formula of each node is:
wherein, KS(t) is the K shell index corresponding to the t-th node, di,tRepresenting the shortest distance between nodes i and t.
Referring to fig. 4, in an embodiment of the invention, a structural diagram of an electronic device is shown.
An embodiment of the present invention provides an electronic device, which may include a processor 310 (CPU), a memory 320, an input device 330, an output device 340, and the like, wherein the input device 330 may include a keyboard, a mouse, a touch screen, and the like, and the output device 340 may include a Display device, such as a Liquid Crystal Display (LCD), a Cathode Ray Tube (CRT), and the like.
The processor 310 is configured to execute the steps of any one of the methods for identifying propagation critical nodes based on K-shell decomposition according to the obtained program instructions by calling the program instructions stored in the memory 320.
Based on the foregoing embodiments, in the embodiments of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, and the computer program, when executed by a processor, implements the method for identifying propagation key nodes based on K-shell decomposition in any of the above method embodiments.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made in the embodiments of the present invention without departing from the spirit or scope of the embodiments of the invention. Thus, if such modifications and variations of the embodiments of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to encompass such modifications and variations.
Claims (8)
1. A method for identifying propagation key nodes based on K-shell decomposition is characterized by comprising the following steps:
establishing a transmission network by collecting message transmission of a social platform, determining a node set according to an individual transmitting a message in the transmission network, and collecting friend list data corresponding to each node in the node set;
obtaining the number of the directly connected edges corresponding to each node according to the friend list data; if the individuals are in a friend relationship, a direct connection edge exists between the two corresponding nodes;
determining the degree of each node in the propagation network according to the number of the directly connected edges of each node, and calculating a K-shell index of each node according to the degree of each node, wherein the K-shell index is used for representing the influence of individuals in the propagation network;
calculating the shortest distance between each pair of nodes in the propagation network by using a Floyd algorithm, and calculating the ranking corresponding score of each node according to the K shell index and the shortest distance so as to obtain a propagation key node in the propagation network;
the formula for calculating the ranking corresponding score of each node is as follows:
wherein, KS(t) is the K shell index corresponding to the t-th node, di,tRepresenting nodes i and tThe shortest distance therebetween.
2. The method for identifying propagation key nodes based on K-shell decomposition as claimed in claim 1, wherein the degree of each node in the propagation network is determined according to the number of directly successive edges of each node, specifically: the degree of a node in the propagation network is equal to the number of directly successive edges of the node.
3. The method for identifying propagation key nodes based on K-shell decomposition as claimed in claim 2, wherein the K-shell index of each node is calculated according to the degree of the node, and the specific steps include:
obtaining degrees of each node in a node set S in the propagation network, wherein the node set S is { S }1,s2,...,snN is the total number of nodes;
traversing the node set S, searching all nodes with the degree of 1, deleting the corresponding nodes and the straight edges of the nodes, and storing the corresponding nodes into the set S1Said set S1K shell index K of each node ins1(p)＝1，p∈S1；
Traversing the node set S, searching all nodes with the degree of 2, deleting the corresponding nodes and the edges connected with the nodes, and storing the corresponding nodes into the set S2Said set S2K shell index K of each node ins2(q)＝2，q∈S2；
Repeatedly traversing the node set S, adding 1 to the searched degree each time until all nodes corresponding to the maximum degree n in the set are searched, deleting the corresponding nodes and the edges connected with the nodes, and storing the corresponding nodes into the set SnSaid set SnK-shell index of each node in the tree
m∈Sn。
4. The K-shell decomposition-based method for identifying propagation critical nodes of claim 3,
the calculating the shortest distance between each pair of nodes in the propagation network by using the Floyd algorithm comprises the following steps:
acquiring a network adjacency matrix G corresponding to the propagation network;
initializing a distance matrix D according to an adjacency matrix G, and if a path from a node i to a node j can be reached, setting the distance matrix D [ i ] [ j ] ═ D, wherein D represents the length of the path; otherwise, D [ i ] [ j ] becomesinfinite, wherein i is more than or equal to 1 and less than j and less than or equal to n;
defining a matrix L to record the information of the inserted node, wherein L [ i ] [ j ] represents the node which needs to pass from the node i to the node j, and initializing L [ i ] [ j ] ═ j; inserting other nodes into the paths from i to j, and comparing the distance from i to j after the nodes are inserted with the original distance, wherein the distance is expressed as: d [ i ] [ j ] ═ min (D [ i ] [ j ], D [ i ] [ k ] + D [ k ] [ j ]), and if the value of D [ i ] [ j ] becomes smaller, L [ i ] [ j ] ═ k; and repeating the operation until the D is not updated any more, and outputting the distance matrix D.
5. An apparatus for identifying propagation key nodes based on K-shell decomposition, comprising:
the system comprises an acquisition module, a transmission module and a processing module, wherein the acquisition module is used for establishing a transmission network by acquiring message transmission of a social platform, determining a node set according to an individual transmitting a message in the transmission network, and acquiring friend list data corresponding to each node in the node set;
the direct connection edge calculation module is used for obtaining the number of the direct connection edges corresponding to each node according to the friend list data; if the individuals are in a friend relationship, a direct connection edge exists between the two corresponding nodes;
the K-shell index calculation module is used for determining the degree of each node in the propagation network according to the number of the directly connected edges of each node, and calculating the K-shell index of each node according to the degree of each node, wherein the K-shell index is used for representing the influence of individuals in the propagation network;
the score calculation module is used for calculating the shortest distance between each pair of nodes in the propagation network by adopting a Floyd algorithm, calculating the ranking corresponding score of each node according to the K shell index and the shortest distance, and further obtaining the propagation key nodes in the propagation network;
in the score calculation module, the formula for calculating the ranking corresponding score of each node is as follows:
wherein, KS(t) is the K shell index corresponding to the t-th node, di,tRepresenting the shortest distance between nodes i and t.
6. The K-shell decomposition-based propagation key node identification apparatus according to claim 5, wherein in the K-shell index calculation module, the degree of nodes in the propagation network is equal to the number of directly connected edges of the nodes.
7. The apparatus for identifying propagation critical nodes based on K-shell decomposition of claim 6, wherein the K-shell index calculation module further comprises:
obtaining degrees of each node in a node set S in the propagation network, wherein the node set S is { S }1,s2,...,snN is the total number of nodes;
traversing the node set S, searching all nodes with the degree of 1, deleting the corresponding nodes and the straight edges of the nodes, and storing the corresponding nodes into the set S1Said set S1K-shell index of each node in the tree
p∈S1；
Traversing the node set S, searching all nodes with the degree of 2, deleting the corresponding nodes and the edges connected with the nodes, and storing the corresponding nodes into the set S2Said set S2K-shell index of each node in the tree
q∈S2；
Repeatedly traversing the node set S, adding 1 to the searched degree each time until all nodes corresponding to the maximum degree n in the set are searched, deleting the corresponding nodes and the edges connected with the nodes, and storing the corresponding nodes into the set SnSaid set SnK-shell index of each node in the tree
m∈Sn。
8. The K-shell decomposition-based apparatus for identifying propagation critical nodes of claim 7,
the calculating the shortest distance between each pair of nodes in the propagation network by using the Floyd algorithm further comprises:
acquiring a network adjacency matrix G corresponding to the propagation network;
initializing a distance matrix D according to an adjacency matrix G, and if a path from a node i to a node j can be reached, setting the distance matrix D [ i ] [ j ] ═ D, wherein D represents the length of the path; otherwise, D [ i ] [ j ] becomesinfinite, wherein i is more than or equal to 1 and less than j and less than or equal to n;
defining a matrix L to record the information of the inserted node, wherein L [ i ] [ j ] represents the node which needs to pass from the node i to the node j, and initializing L [ i ] [ j ] ═ j; inserting other nodes into the paths from i to j, and comparing the distance from i to j after the nodes are inserted with the original distance, wherein the distance is expressed as: d [ i ] [ j ] ═ min (D [ i ] [ j ], D [ i ] [ k ] + D [ k ] [ j ]), and if the value of D [ i ] [ j ] becomes smaller, L [ i ] [ j ] ═ k; and repeating the operation until the D is not updated any more, and outputting the distance matrix D.
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