Conflict graph based Community Detection

Abstract

Community is a network's subgraph where vertices share similar properties and reflect interesting characteristics for understanding complex networks more closely. Therefore, community structure analysis is important in understanding and exploring complex networks and helps in describing relationship among nodes in a network. However, efficiently finding communities in a complex network still remains an open problem. Since there exists numerous ways of defining a community, existing strategies have adopted different parameters to reflect varied behavior of a community structure and trying to give a coarser or finer community distribution. In this paper, we propose Conflict graph Transform based Community Detection (CTCD) strategy to improve the quality of community distributions. CTCD focuses on the impact of degree of influence to detect more favorable community partitions. A well known measure, known as Surprise, is used to evaluate and compare the quality of the community distributions obtained using CTCD. Finally, in order to study the performance and usefulness of our strategy, CTCD is applied in real-world networks. Using CTCD, we are able to obtain better community distributions with higher Surprise value in real-world networks. We observe that 1-hop and 2-hop influences improve the Surprise value in higher and lower average clustering coefficient networks, respectively. Moreover, CTCD can efficiently extract the hierarchical nature of communities within networks.