Rlapping Community Detection Based On Membership Degree Propagation

Complex network is the natural expression of many complex problems,so complex network can be used to solve many problems in the real world.Complex networks have different forms in many different fields,such as social networks,gene regulatory networks,protein interaction networks,etc.A key feature of complex networks is their community structure.Community structure can be regarded as a kind of "network cluster".In a complex network,the nodes in the same community are closely connected with each other,and a large number of internal edges exist within an individual community,while the connections between nodes in different communities are sparse,and normally only a small number of external edges connect to different communities.In general,the nodes belonging to the same communities have similar functions or properties and vice versa.For example,the nodes in a same community of social network often represent that they might have a same family,a same career,or a same hobby,while those of a protein-protein interaction network are probably the proteins with similar functions.In a complex network,there are interactions between different communities,with an important form that different communities share the same nodes.We call these nodes overlapping nodes and these communities as overlapping communities.Overlapping nodes and overlapping communities exist widely in complex networks in the real world.For example,one individual may be in multiple communities(e.g.families)of a social network.In the biomolecular network,different communities can represent different biological functions,and a gene or protein can participate in a variety of biological functions.In the academic circle,a scholar often works on multiple fields.Overlapping nodes often play an important role in complex networks.Because overlapping nodes belong to and connect multiple overlapping communities,and play a pivotal role in information flow,the identification of overlapping nodes is an important research topic in complex network analyses.By introducing a concept of membership degree,this paper proposed an overlapping community detection algorithm based on membership degree propagation.In this algorithm,membership degree propagation is driven by both global and local information for the division of the node community.The proposed algorithm was applied to synthetic LFR datasets and real-world datasets,and compared with other existing up-to-date algorithms.Experimental results show that our proposed algorithm is effective and outperforms the comparison methods on most datasets.In particular,it significantly improved the performance of the overlapping node prediction.In summary,the main work of this paper is as follows: a new overlapping community discovery algorithm based on membership propagation is proposed,and the main steps of the algorithm are described in detail;experiments are carried out on LFR benchmark datasets and real-world datasets,and the community partition results are compared with other community partition algorithms;the partition results are analyzed in detail by using the NMI,Omega index,module degree and other methods.The experimental results show that compared with the existing algorithms,the proposed algorithm improves the efficiency of community discovery and the recognition effect of overlapping nodes.