Extended core-based community detection for directed networks

The focus of this paper is on detecting overlapping communities for the directed graphs by implementing a new algorithm and analyzing it with various performance metrics. The algorithm aims at finding core nodes for the directed graph which are subset of communities and have higher contact frequency. These are then extended to find communities using compactness measurement (CM). The compactness of a node to the community is defined as the ratio of the outward degree of the node to the community to that of the total out degree of that node. Another approach that will be used to extend communities around core nodes is based on similarity measurement (SM) - two nodes are said to be similar if they share more mutual neighbours. We are able to achieve a success rate of 70% when CM is used and about 10–15% with SM based expansion method. The proposed algorithm is also compared with the existing method for community detection.

[1]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  Stephen G. Kobourov,et al.  Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale , 2016, PloS one.

[3]  C. Bron,et al.  Algorithm 457: finding all cliques of an undirected graph , 1973 .

[4]  Junming Shao,et al.  Community Detection based on Distance Dynamics , 2015, KDD.

[5]  Sune Lehmann,et al.  Link communities reveal multiscale complexity in networks , 2009, Nature.

[6]  Wei Wang,et al.  A Core-based Community Detection Algorithm for Networks , 2010, 2010 International Conference on Computational Aspects of Social Networks.

[7]  Steve Gregory,et al.  Finding overlapping communities in networks by label propagation , 2009, ArXiv.

[8]  S.,et al.  An Efficient Heuristic Procedure for Partitioning Graphs , 2022 .

[9]  Coenraad Bron,et al.  Finding all cliques of an undirected graph , 1973 .

[10]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  Illés J. Farkas,et al.  CFinder: locating cliques and overlapping modules in biological networks , 2006, Bioinform..

[13]  Fergal Reid,et al.  Detecting highly overlapping community structure by greedy clique expansion , 2010, KDD 2010.

[14]  Andrea Lancichinetti,et al.  Detecting the overlapping and hierarchical community structure in complex networks , 2008, 0802.1218.

[15]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[16]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  Yun Chi,et al.  Combining link and content for community detection: a discriminative approach , 2009, KDD.

[18]  Soom Satyam Behera,et al.  Detecting Communities in Networks and Performance Prediction Based on Relation Strength Measurement , 2016 .

[19]  M. Randic,et al.  Resistance distance , 1993 .