2015/06/18 by Radhika Arava, Arava, Radhika
Computer Science · Physics and Astronomy · #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Physical sciences #Opinion Dynamics and Social Influence #Peer-to-Peer Network Technologies #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1506.05659
openalex publication_date 2015/06/18 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
The problem of community detection is important as it helps in understanding the spread of information in a social network. All real complex networks have an inbuilt structure which captures and characterizes the network dynamics between its nodes. Linkages are more likely to form between similar nodes, leading to the formation of some community structure which characterizes the network dynamic. The more friends they have in common, the more the influence that each person can exercise on the other. We propose a disjoint community detection algorithm, NashDisjoint that detects disjoint communities in any given network. We evaluate the algorithm NashDisjoint on the standard LFR benchmarks, and we find that our algorithm works at least as good as that of the state of the art algorithms for the mixing factors less than 0.55 in all the cases. We propose an overlapping community detection algorithm NashOverlap to detect the overlapping communities in any given network. We evaluate the algorithm NashOverlap on the standard LFR benchmarks and we find that our algorithm works far better than the state of the art algorithms in around 152 different scenarios, generated by varying the number of nodes, mixing factor and overlapping membership. We run our algorithm NashOverlap on DBLP dataset to detect the large collaboration groups and found very interesting results. Also, these results of our algorithm on DBLP collaboration network are compared with the results of the COPRA algorithm and OSLOM.