基于网络嵌入与局部合力的复杂网络社区划分算法

doi:10.11896/jsjkx.200200102

Abstract

Abstract: Community detection can reveal the inherent structure dynamic behavior in complex networks and it is the current research hotspot.In this paper,we propose a community detection algorithm based on network embedding and local resultant force.The network topological space is transformed into euclidean space,and network nodes are converted into vector data points.First,based on the gravity model and the network topology,a local resultant force and a local resultant force cosine centrality index(LFC) are proposed.The center node of each initial small community is determined by the LFC of the node and the distance between nodes.Then the rest of the non-central nodes are classified to the nearest central node to form the initial small community.Finally,communities are merged by optimizing the modularity to find the optimal network community structure.Compared with 6 well-known community detection algorithms on 6 real-world networks and artificial networks with adjustable parameters,the new proposed algorithm shows good performance in the community detection.

Key words: Community detection, Gravity model, Local resultant force, Local resultant force cosine centrality, Network embedding

CLC Number:

TP391

YANG Xu-hua, WANG Chen. Community Detection Algorithm in Complex Network Based on Network Embedding and Local Resultant Force[J].Computer Science, 2021, 48(4): 229-236.

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