Influence Maximization of Nodes Based on Leiden Community Division
With the arrival of the big data time and the rapid growth of network size,there is an ur-gent need to study efficient methods for solving influence maximization problems in large-scale social network environments.Moreover,real social networks often have obvious community structure,and find-ing influence nodes within each community can effectively reduce the computational cost.To this end,the community partitioning algorithm is proposed to divide large-scale social networks into multiple communities so that the search space is significantly reduced,and the influence maximization study is carried out on this basis as follows:firstly,the community network is partitioned by modularity using Leiden's algorithm;secondly,the top-k seed nodes are selected within each community using the im-proved PageRank algorithm based on centrality;finally,the final influence scale of seed nodes on the SIR model was tested.Through extensive experimental studies on five real social network datasets,the validation shows the efficiency and effectiveness of the proposed scheme.
influence maximizationLeiden algorithmPageRank algorithmSIR model
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