Identification of top-K influential communities in big networks

Abstract

Because communities are the fundamental component of big data/large data network graphs, community detection in large-scale graphs is an important area to study. Communities are a collection of a set of nodes with similar features. In a given graph there can be many features for clustering the nodes to form communities. Varying the features of interest can form several communities. Out of all communities that are formed, only a few communities are dominant and most influential for a given network graph. This might well contain influential nodes; i.e., for each possible feature of clustering, there will be only a few influential communities in the graph. Identification of such communities is a salient subject for research. This paper present a technique to identify the top-K communities, based on the average Katz centrality of all the communities in a network of communities and the distinctive nature of the communities. One can use these top-K communities to spread information efficiently into the network, as these communities are capable of influencing neighboring communities and thus spreading the information into the network efficiently.