Research On Importance Ranking And Impact Maximization Of Complex Network Nodes

With the rapid development of Internet technology,complex networks have gradually become an important medium for people to get information.In recent years,the analysis and research of complex networks has been favored by many scholars.Particularly,the order of importance of nodes and the maximization of impact have great significance.The following will discussed the two questions.Firstly,for the K-shell algorithm to divide the problem of high coarse grain size,a new index is given to replace the original Ks value,and the influence of the nodes on the same layer is further distinguished.Considering the influence of node information control ability on its importance,the node information control ability metric based on structural hole theory is given.Finally,using the two indicators given above,combined with the node local propagation ability,a new node importance ranking algorithm is proposed.The algorithm can comprehensively evaluate the importance of the node from three aspects: the global position of the node,the local propagation ability and the information control capability.Secondly,aiming at the problem of low efficiency of greedy algorithm selection node process,the local update mechanism of influence is given.Combined with the characteristics of community structure of network,the algorithm of maximizing the influence of community attribute and greedy thought is proposed.The algorithm selects the seed node in two stages.The first stage is the heuristic selection stage.The community is divided into communities.The community is used as the unit.The nodes in the community are ranked by the node importance ranking algorithm proposed above to obtain the candidate nodes.The set and the first part of the seed node.The second stage is the greedy selection stage.The influence local update mechanism is used to select the remaining seed nodes in the candidate node set.Combine the two partial seed nodes to form the final seed node set.Finally,different evaluation criteria are used for the proposed two algorithms on the SIR model and the independent cascade model,and the simulation experiments are compared with other algorithms.The experimental results show that the proposed node importance ranking algorithm is better than other algorithms.The seed set selected by the fusion community attribute and greedy thought maximization algorithm can make the information spread faster and wider.