融入模体信息的多层网络社区发现算法OA北大核心CSTPCD

The multi-layer network community discovery algorithm aims to reveal the community structure of complex networks and has received widespread attention in recent years.However,existing algorithms only focus on the low-order structural information between nodes when measuring node similarity,ignoring the utilization of high-order structural information.Moreover,when fusing information from different layers of the network,there is a lack of consideration for the differences between different layers.To address these issues,the paper proposes a multi-layer network community discovery algorithm incorporating motif information.Specifically,firstly,each layer calculates a high-order adjacency matrix based on the motif information,fuses it with a adjacency matrix to obtain a reconstruction matrix,and then enhances the reconstruction matrix based on the importance of node neighbors to obtain a similarity matrix between nodes.Secondly,based on the reconstruction matrix,the importance of each layer of the network is calculated,and weighted fusion is used to obtain a unified similarity matrix.Finally,based on the obtained similarity matrix,the node influence is calculated,and the vector representation of the nodes is iteratively updated through the node embedding representation method to obtain the final embedding representation.Comparative experiments were conducted with existing multi-layer network community discovery algorithms on artificial multi-layer networks and real multi-layer network data.The results indicate that the proposed algorithm outperforms existing algorithms in terms of multi-layer modularity and normalized mutual information.