CoDiS

Abstract

Community detection has been and remains a very important topic in several fields. From marketing and social networking to biological studies, community detec- tion plays a key role in advancing research in many different fields. Research on this topic originally looked at classifying nodes into discrete communities, but eventually moved forward to placing nodes in multiple communities. Unfortunately, community detection has always been a time-inefficient process, and recent data sets have been simply to large to realistically process using traditional methods. Because of this, recent methods have turned to parallelism, but all these methods, while offering sig- nificant decrease in processing time, still have several issues. The innovation of this paper is that it distributes the seed nodes instead of the stream edges, and therefore assigns to each working node a subset of the current formed communities. Experi- mental results show that we are able to gain a significant improvement in running time with no loss of accuracy.