CN116756207A - Network key node mining method based on discount strategy and improved discrete crow search algorithm - Google Patents
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Abstract
The application relates to the technical field of computer complex network optimization, and discloses a network key node mining method based on discount strategies and an improved discrete crow search algorithm, which comprises the following steps: firstly, preprocessing a quotation network, converting the quotation network into an adjacent matrix, and reversing the network to obtain a reverse network; then, the LRDiscount algorithm is utilized to carry out initial screening on the nodes of the reverse network to obtain a candidate node setCThe method comprises the steps of carrying out a first treatment on the surface of the Next, a set of candidate nodes is subjected to a local optimization process according to an improved discrete crow search algorithmCOptimizing; finally, selecting an optimal set from the optimized node set, and evaluating the influence of the nodes to obtain the final resultkAnd key seed nodes. Compared with the prior art, the method and the device have the advantages that the influence discount strategy of the network node and the improved discrete crow search algorithm are adoptedAnd combining, performing influence diffusion from updating node positions in the simulated crow searching process, and searching for key nodes through marginal gains generated by the migration of crow individuals in the quotation network.
Description
Technical Field
The application belongs to the technical field of complex network influence maximization, and particularly relates to a network key node mining method based on a discount strategy and an improved discrete crow search algorithm.
Background
Along with the penetration of various mobile social services to human life and social contact, the social network plays a non-negligible role in the aspects of information sharing, information propagation and diffusion and the like. The large-scale social network brings great challenges to the research of the traditional problem of maximizing the influence, and also brings the research of the problem with greater practical significance, while the IM (Influence Maximization, IM) aims at solving K influencing nodes (the nodes represent social media users) in the social network, and propagates information by using the 'public praise' effect, so that the influence range of the nodes is maximized. Therefore, how to select K nodes in the network while ensuring time complexity and propagation effect is a major challenge faced by the problem of maximizing impact.
The key to the impact maximization problem is how to select key nodes in the network, which has been of interest to researchers since the 20 th century. In the field of information science, with the deep research of information transmission and diffusion problems in social media, the key nodes play an extremely important role in the research of dynamic evolution, transmission control and the like of a network structure. Some researchers apply meta-heuristic search algorithms to network key node mining, but the disadvantage is that the algorithms are slow to execute and have high time complexity, and cannot be applied to large-scale networks. Therefore, based on the research field of the algorithm, zhang et al considers that neighbor nodes play an important role in measuring the influence of the nodes, and provides a heuristic algorithm PRDiscount combined with Pagerank, which definitely discounts the influence of all individuals with social relationship with selected seeds. However, although the meta-heuristic algorithm improves the optimization efficiency of mining the key nodes to a certain extent, the single-solution algorithm has only one solution in the iterative process, has the advantages of simplicity and rapidness for solving the problem of mining the key nodes of a small-scale network, and is easy to sink into local optimum, thereby causing network information redundancy. Then Gong et al propose a key node identification method based on a discrete particle swarm optimization algorithm, which defines the position of a particle as a node number, defines the speed of the particle as a sign for judging whether the node is updated, and further finds out a global optimal particle Gbest under the limitation of search conditions through multiple iterations, wherein the position of the particle is the found optimal seed node set. Therefore, in order to overcome the disadvantages of the algorithm, the current research of maximizing the influence on searching key nodes is combined with the intelligent optimization algorithm of the population, which simulates the evolution process of the cooperative behavior or physical phenomenon of the biological population, and is widely applied to the optimization problem of maximizing the influence in recent years due to the strong heuristic search thinking and global search capability.
Therefore, how to design a method with high accuracy and low cost, it is extremely important to obtain seed nodes in a large-scale network.
Disclosure of Invention
The application aims to: aiming at the problems of large network scale, large data volume and the like in the traditional network key node mining, so that the key node mining is low in efficiency and high in time complexity, the application provides a network key node mining method based on discount strategies and improved discrete crow search algorithms, wherein the influence discount strategies of network nodes are combined with the improved discrete crow search algorithms, influence diffusion is carried out by updating node positions in the simulated crow search process, and key nodes are searched through marginal gains generated by the migration of crow individuals in a citation network.
The technical scheme is as follows: the application provides a network key node mining method based on a discount strategy and an improved discrete crow search algorithm, which comprises the following steps:
s1, preprocessing a quotation network, converting a target network to obtain an adjacent matrix of the target network, and performing reverse operation on the quotation network to obtain a reverse network;
s2, discounting the influence of each node pointing to the seed node in the reverse network in the S1 according to a discount policy algorithm LRDisunt, obtaining the discounted influence of the node, sequentially selecting the node with the largest value and adding the node to the candidate node set C;
s3, optimizing a candidate node set C by utilizing a local optimization process of an improved discrete crow search algorithm, wherein the candidate node set is a candidate crow group, the improved discrete crow search algorithm adds parallelization iteration processing on the basis of a traditional discrete crow search algorithm, namely, the discrete crow search is only compared with a memory vector corresponding to a crow individual during each iteration, so that the memory vector is updated to meet the update of the position vector of the crow individual, and the optimized node set C is obtained * ；
S4, node set C after optimization * And selecting an optimal set, and evaluating the influence of the nodes, so as to obtain final k key seed nodes.
Further, the specific step of obtaining the candidate node set C in the step S2 is:
s2.1, adding a background node bg in a reverse network, and connecting the background node bg with all nodes in the network, so as to obtain a new network of N+1 nodes which are strongly communicated;
s2.2, distributing LR values of 1 unit to N nodes except the background node bg, wherein the LR value of the background node bg is 0;
s2.3, equally distributing the LR value of 1 unit to directly connected neighbor nodes, and continuously iterating until reaching a stable state:
wherein ,for node v j Degree of departure, w ji For adjacency matrix elements, representing node v j And node v i Where there is an edge between, then w ji =1, otherwise 0;
s2.4, after the iteration is finished, the LR value LR of the background node bg is calculated bg (t c ) Equally dividing to all nodes in the network to obtain node v i Final LR values of (a);
s2.5, a discount policy algorithm LRDiscount discounts influence of neighbor nodes of each seed node, namely, in a reverse network, discounts influence of each node pointing to the seed node, so that discounted influence of the node is obtained:
wherein S is a seed node set,representing node v i The number of seed nodes in the neighbor nodes accounts for the ratio of the number of all the neighbor nodes;
and S2.6, according to the finally obtained node influence, sequentially selecting the node with the largest value in the INF and adding the node into the candidate node set C.
Further, the specific step of optimizing the candidate node set C by using the local optimization process of the improved discrete crow search algorithm in the step S3 is as follows:
s3.1, initializing all data in an IDCSA (discrete crow search algorithm framework), wherein the crow group scaleN is the seed node set to be solved is k, and the maximum iteration number t max Sensing probability AP and local search node neighbor domain range S initial parameters;
s3.2, taking the candidate node set C obtained in the step S2 as a candidate crow group, and initializing a position vector x of the crow group i ＝(node 1 ，node 2 ，...，node n ) Memory vector Memory t-1 ＝[m 1 ，m 2 ，...，m n ] -1 The method comprises the steps of carrying out a first treatment on the surface of the Selecting an initial optimal solution position vector X from the initialized population;
s3.3, constructing a discretization search rule of a network space based on node coding and discretization representation of the crow group position vector and the memory vector:
wherein ,R(ri S) is a local search mechanism, and the symbol "≡" is defined as a logical cross operation, which aims at comparing whether there are duplicate nodes in the two position vectors;
s3.4, defining an objective function to calculate function fitting values of N crow individuals based on the node pool of the candidate node set C generated in the step S2, and performing approximate evaluation on the influence of seed nodes in the crow position vector evolution by adopting a local influence evaluation function LIE:
s3.5, carrying out local optimization search on 2-order neighbor nodes of each node in the candidate node set C according to an improved discrete crow search algorithm, if the marginal benefit value of a certain 2-order neighbor node is large relative to the marginal benefit of the node, replacing the node in the current optimal solution by the 2-order neighbor node, and repeatedly executing until the maximum iteration number t is reached max The upper limit is reached.
Further, the specific steps of performing the local optimization search on the node set according to the improved crow search algorithm in the step S3.5 are as follows:
1) Calculating the optimal position vector difference between the current crow individual i and the tracked crow individual jOn the basis, the cross operation is carried out to obtain a decision vector V node Judging whether to perform local search optimization or not;
2) Node x i Storing the first-order direct neighbor nodes of each node into a node set neighbor, traversing the nodes in sequence, finding out a 2-hop neighbor node set of the node, incorporating the 2-hop neighbor node set into the node set, and repeating the node set until the first-order neighbor nodes of each node are traversed, so as to ensure that the 2-hop neighbor nodes have no repeated nodes;
3) Sequentially calculating seed subset position vectors x i LIE adaptation value after the node at the corresponding position is replaced by the neighboring node, and selecting the neighboring node set NodeSet to be able to x i The node bringing the maximum benefit in the vector will correspond to x i The nodes in the vector are replaced.
Further, the local search optimization iteration number in S4 reaches t mmax The node set C obtained after * Node set C * And selecting k key node sets through an influence maximization algorithm.
The beneficial effects are that:
the application discloses a method for mining key nodes based on discount strategies and improving discrete crow search algorithms, which solves the problems of slow algorithm convergence and low comprehensive influence of optimal seed subsets, and is specifically expressed as follows:
(1) And the mutual influence among the nodes in the quoted network is discounted by an LRDiscount algorithm, and the network topology structure and the node attribute information are fully utilized.
(2) In the local search optimization of the discrete crow search algorithm, the network node discount strategy is considered, so that the seed set in the initial activation state is screened out, and the defects of the convergence speed of the algorithm and the good and bad results of the initial seed node set are avoided.
(3) Cross operation is added in the discrete crow group search rule, so that the richness of the population in the search process is maintained, the situation that a local optimal solution is trapped is avoided, and the updated optimal node is ensured to have no repeated node in the vector xi.
Aiming at the key node mining problem, the method firstly converts the target problem into the optimization problem, then screens the initial seed set by utilizing the proposed network node discount strategy, and then improves the discrete crow search algorithm to carry out optimization solution on the final seed set. According to the method, key nodes in the complex network are searched in the research of optimizing the problem of maximizing the future influence, and better effects can be obtained under the same conditions.
Drawings
FIG. 1 is an overall flow chart of the present application;
FIG. 2 is a sub-flowchart of the LRDiscount algorithm of FIG. 1;
FIG. 3 is a partial optimization process sub-flowchart of the improved discrete crow search algorithm of FIG. 1.
Detailed Description
The application is further elucidated below in connection with the drawings and the detailed description. It is to be understood that these examples are for the purpose of illustrating the application only and are not to be construed as limiting the scope of the application, since modifications to the application, which are various equivalent to those skilled in the art, will fall within the scope of the application as defined in the appended claims after reading the application.
As shown in fig. 1, the specific steps of the network key node mining method based on the discount strategy and the improved discrete crow search algorithm disclosed by the application are as follows:
s1, preprocessing a quotation network, converting the target network to obtain an adjacent matrix of the target network, and performing reverse operation on the quotation network to obtain a reverse network.
S2, discounting the influence of each node pointing to the seed node in the reverse network in S1 according to a discount policy algorithm LRDisunt to obtain the discounted influence of the node, sequentially selecting the node with the largest value and adding the node to a candidate node set C, wherein the specific steps are as follows:
s2.1, in the reverse network obtained in the step S1, a background node bg is added to be connected with all nodes in the network, so that a new network of N+1 nodes with strong communication is obtained.
S2.2, distributing LR values of 1 unit to N nodes except the background node bg, wherein the LR value of the background node bg is 0:
s2.3, equally distributing the LR value of 1 unit to directly connected neighbor nodes, and continuously iterating until reaching a stable state:
wherein ,for node v j Degree of departure, w ji For adjacency matrix elements, representing node v j And node v i Where there is an edge between, then w ji =1, otherwise 0.
S2.4, after the iteration is finished, the LR value LR of the background node bg is calculated bg (t c ) Equally dividing to all nodes in the network to obtain node v i Final LR value of (c):
s2.5, a discount policy algorithm LRDiscount discounts influence of neighbor nodes of each seed node, namely, in a reverse network, discounts influence of each node pointing to the seed node, so that discounted influence of the node is obtained:
wherein S is a seed node set,representing node v i The number of seed nodes in the neighbor nodes of (a) is the ratio of the number of all neighbor nodes.
And S2.6, according to the finally obtained node influence, sequentially selecting the node with the largest value in the INF and adding the node into the candidate node set C.
S3, optimizing a candidate node set C by utilizing a local optimization process of an improved discrete crow search algorithm, wherein the candidate node set is a candidate crow group, the improved discrete crow search algorithm adds parallelization iteration processing on the basis of a traditional discrete crow search algorithm, namely, the discrete crow search is only compared with a memory vector corresponding to a crow individual during each iteration, so that the memory vector is updated to meet the update of the position vector of the crow individual, and the optimized node set C is obtained * As shown in fig. 2, the specific steps are as follows:
s3.1, initializing all data in an IDCSA (discrete crow search algorithm framework), wherein the crow group scale is N, the seed node set to be solved is k, and the maximum iteration number t is achieved max The method comprises the steps of sensing initial parameters such as probability AP, local search node near neighborhood range S and the like.
S3.2, initializing a position vector x of the crow group according to the candidate node set C obtained in the step S2 i ＝(node 1 ，node 2 ，...，node n ) Memory vector Memory t-1 ＝[m 1 ，m 2 ，...，m n ] -1 The method comprises the steps of carrying out a first treatment on the surface of the And selecting an initial optimal solution position vector X from the initialized population * 。
S3.3, constructing a discretization search rule of a network space based on node coding and discretization representation of the crow group position vector and the memory vector:
wherein ,R(ri S) is a local search mechanism, the symbol ". U" is defined as a logical crossover operation that aims at comparing whether there is a duplication of two position vectorsAnd (5) a node.
S3.4, defining an objective function to calculate function fitting values of N crow individuals based on the node pool of the candidate node set C generated in the step S2, and performing approximate evaluation on the influence of seed nodes in the crow position vector evolution by adopting a local influence evaluation function LIE:
s3.5, carrying out local optimization search on 2-order neighbor nodes of each node in the candidate node set C according to an improved discrete crow search algorithm, if the marginal benefit value of a certain 2-order neighbor node is large relative to the marginal benefit of the node, replacing the node in the current optimal solution by the 2-order neighbor node, and repeatedly executing until the maximum iteration number t is reached max The upper limit is reached. The specific process is as follows:
firstly, calculating the optimal position vector difference between the current crow individual i and the tracked crow individual jOn the basis, the cross operation is carried out to obtain a decision vector V node And judging whether to perform local search optimization or not.
Then node x i Storing the first-order direct neighbor nodes of each node into a node set neighbor, traversing the nodes in sequence, finding out a 2-hop neighbor node set of the node set, incorporating the node set into the node set, and repeating the node set until the first-order neighbor nodes of each node are traversed, so as to ensure that the 2-hop neighbor nodes do not have repeated nodes.
Finally, calculating seed set position vector x in turn i LIE adaptation value after the node at the corresponding position is replaced by the neighboring node, and selecting the neighboring node set NodeSet to be able to x i The node bringing the maximum benefit in the vector will correspond to x i The nodes in the vector are replaced.
S4, node set C after optimization * Selecting an optimal set, and evaluating the influence of the nodes to obtain final k key seed nodes。
The application can be combined by a computer system, thereby completing the excavation of the seed nodes.
The network key node mining method based on the discount strategy and the improved discrete crow search algorithm can be used for mining key nodes in complex networks with different scales.
The foregoing embodiments are merely illustrative of the technical concept and features of the present application, and are intended to enable those skilled in the art to understand the present application and to implement the same, not to limit the scope of the present application. All equivalent changes or modifications made according to the spirit of the present application should be included in the scope of the present application.
Claims (5)
1. A network key node mining method based on discount strategy and improved discrete crow search algorithm is characterized by comprising the following steps:
s1, preprocessing a quotation network, converting a target network to obtain an adjacent matrix of the target network, and performing reverse operation on the quotation network to obtain a reverse network;
s2, in the reverse network described in S1, discount calculation is carried out on the node influence of the network according to an LRDiscount algorithm, so that the node influence after discount is obtained, and the node with the largest value is sequentially selected and added to the candidate node set C;
s3, optimizing a candidate node set C by utilizing a local optimization process of an improved discrete crow search algorithm, wherein the candidate node set is a candidate crow group, the improved discrete crow search algorithm adds parallelization iteration processing on the basis of a traditional discrete crow search algorithm, namely, the discrete crow search is only compared with a memory vector corresponding to a crow individual during each iteration, so that the memory vector is updated to meet the update of the position vector of the crow individual, and the optimized node set C is obtained * ；
S4, finally, from the optimized node set C * And selecting an optimal set, and evaluating the influence of the nodes, so as to obtain final k key seed nodes.
2. The network key node mining method based on the discount policy and the improved discrete crow search algorithm according to claim 1, wherein the specific steps of obtaining the candidate seed subset C in the step S2 are as follows:
s2.1, adding a background node bg in a reverse network, and connecting the background node bg with all nodes in the network, so as to obtain a new network of N+1 nodes which are strongly communicated;
s2.2, distributing LR values of 1 unit to N nodes except the background node bg, wherein the LR value of the background node bg is 0;
s2.3, equally distributing the LR value of 1 unit to directly connected neighbor nodes, and continuously iterating until reaching a stable state:
wherein ,for node v j Degree of departure, w ji For adjacency matrix elements, representing node v j And node v i Where there is an edge between, then w ji =1, otherwise 0;
s2.4, after the iteration is finished, the LR value LR of the background node bg is calculated bg (t c ) Equally dividing to all nodes in the network to obtain node v i Final LR values of (a);
s2.5, a discount policy algorithm LRDiscount discounts influence of neighbor nodes of each seed node, namely, in a reverse network, discounts influence of each node pointing to the seed node, so that discounted influence of the node is obtained:
wherein S is a seed node set,representing node v i The number of seed nodes in the neighbor nodes accounts for the ratio of the number of all the neighbor nodes;
and S2.6, according to the finally obtained node influence, sequentially selecting the node with the largest value in the INF and adding the node into the candidate node set C.
3. The network key node mining method based on the discount policy and the improved discrete crow search algorithm according to claim 1, wherein the specific steps of optimizing the candidate node set C by using the local optimization process of the improved discrete crow search algorithm in the step S3 are as follows:
s3.1, initializing all data in an IDCSA (discrete crow search algorithm framework), wherein the crow group scale is N, the seed node set to be solved is k, and the maximum iteration number t is achieved max Sensing probability AP and local search node neighbor domain range S initial parameters;
s3.2, using the candidate node set C obtained in the step S2 as a candidate crow group, thereby initializing a position vector x of the crow group i ＝(node 1 ,node 2 ,…,node n ) Memory vector Memory t-1 ＝[m 1 ,m 2 ,…,m n ] -1 The method comprises the steps of carrying out a first treatment on the surface of the And selecting an initial optimal solution position vector X from the initialized population * ；
S3.3, constructing a discretization search rule of a network space based on node coding and discretization representation of the crow group position vector and the memory vector:
wherein ,R(ri S) is a local search mechanism, and the symbol "≡" is defined as a logical cross operation, which aims at comparing whether there are duplicate nodes in the two position vectors;
s3.4, defining an objective function to calculate function fitting values of N crow individuals based on the node pool of the candidate node set C generated in the step S2, and performing approximate evaluation on the influence of seed nodes in the crow position vector evolution by adopting a local influence evaluation function LIE:
s3.5, carrying out local optimization search on 2-order neighbor nodes of each node in the candidate node set C according to an improved discrete crow search algorithm, if the marginal benefit value of a certain 2-order neighbor node is large relative to the marginal benefit of the node, replacing the node in the current optimal solution by the 2-order neighbor node, and repeatedly executing until the maximum iteration number t is reached max The upper limit is reached.
4. The network key node mining method based on the discount policy and the improved discrete crow search algorithm according to claim 3, wherein the specific steps of performing the local optimization search on the node set according to the improved crow search algorithm in step S3.5 are as follows:
1) Calculating the optimal position vector difference between the current crow individual i and the tracked crow individual jOn the basis, the cross operation is carried out to obtain a decision vector V node Judging whether to perform local search optimization or not;
2) Node x i Storing the first-order direct neighbor nodes of each node into a node set neighbor, traversing the nodes in sequence, finding out a 2-hop neighbor node set of the node, incorporating the 2-hop neighbor node set into the node set, and repeating the node set until the first-order neighbor nodes of each node are traversed, so as to ensure that the 2-hop neighbor nodes have no repeated nodes;
3) Sequentially calculating seed subset position vectors x i LIE adaptation value after the node at the corresponding position is replaced by the neighboring node, and selecting the neighboring node set NodeSet to be able to x i The node bringing the maximum benefit in the vector will correspond to x i The nodes in the vector are replaced.
5. The network key node mining method based on discount policies and improved discrete crow search algorithm as recited in claim 1, wherein the number of local search optimization iterations in S4 reaches t max The node set C obtained after * Node set C * And selecting k key node sets through an influence maximization algorithm.
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| CN117811992B (en) * | 2024-02-29 | 2024-05-28 | 山东海量信息技术研究院 | Network bad information propagation inhibition method, device, equipment and storage medium |
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