A Parallel Approach to Detect Communities in Evolving Networks

摘要

To understand the dynamics, functional and topological aspect of the real-world networks it is necessary to segregate the network into sub-networks, where each member of a sub-network possess analogous characteristics. Numerous number of community finding approaches are proposed in the last few decades to overcome the issues associated with community detection. Although, most of the conventional approaches rely on the premises that networks are static in nature and there won't be any alternation over time. Moreover, all these approaches are single machine approach and hence exhibits poor scalability. In this work, we propose a new incremental parallel community detection method, PcDEN (Parallel Community Detection approach in Evolving Networks). Our proposed method can detect communities in dynamic distributed networks. We define a new Affinity score based on intra-community strength between nodes and their neighbors. We also derive a new model to perform community merging, based on common high degree nodes present in both the communities. We tested our algorithm on various real-world networks for our experimentation. Results show that, PcDEN produce satisfactory output with respect to various assessment indices.