Overlapping community detection using a community optimized graph swarm
Detection of communities within social networks is a nontrivial problem. Allowing communities to overlapi.e. nodes can belong to more than one community simultaneouslyfurther complicates the problem. Nevertheless, people do belong to multiple social groups simultaneously and being able to detect overlapping communities is an important step into being able to understand and analyze social networks. A common practice in community detection (clustering) is to view the network (graph) as a whole and have a central control process determine how nodes are clustered. That central control, we believe, is a limitation to performance. In our previous work, we showed that the individuals view of his or hers social groups could be aggregated to produce communities. In this paper, we propose a unique approach to community detection that combines the individuals view of a community, not having the view the graph as a whole, with swarm intelligence as a means of removing the central control mechanism. Our approach offers a community detection solution that finds overlapping communities while running in O(n log 2 n) time.
community detection social networks swarm intelligence community clustering complex networks multi-agent system