community detection

The discovery and analysis of community structures in networks has attracted increasing attention in recent years. However there are some well-known quality metrics for detecting and evaluating communities, each of them has its own limitations. In this paper, we first deeply discuss these limitations for community detection and evaluation based on the definitions and formulations of these quality metrics. Then, we perform some experiments on the artificial and real-world networks to demonstrate these limitations. Analyzed quality metrics in this paper include modularity, performance, coverage, normalized mutual information (NMI), conductance, internal density, triangle participation ratio and cut ratio. Comparing with previous works, we go through the limitations of modularity with much more accurate details. Moreover, for the first time, we present some limitations of NMI.  In addition, however it is known that performance has tendency to get high values in large graphs, we explore this limitation by its formulation and discuss several specific cases in which performance even on small graphs gets high scores

Social network research analyzes the relationships between interactions, people, organizations, and entities. With the developing reputation of social media, community detection is drawing the attention of researchers. The purpose of community detection is to divide social networks into groups. These communities are made of entities that are very closely related. Communities are defined as groups of nodes or summits that have strong relationships among themselves rather than between themselves. The clustering of social networks is important for revealing the basic structures of social networks and discovering the hyperlink of systems on human beings and their interactions. Social networks can be represented by graphs where users are shown with the nodes of the graph and the relationships between the users are shown with the edges. Communities are detected through clustering algorithms. In this paper, we proposed a new clustering algorithm that takes into account the extent of relationships among people. Outcomes from particular data suggest that taking into account the profundity of people-to-people relationships increases the correctness of the aggregation methods.