Detecting and Exploring Homogeneous Dense Groups via k-Core Decomposition and Core Member Filtering in Social Networks

Exploring homogeneous dense groups is one of the important issues in social network structure measurement. k-core decomposition and core member filtering are common methods to uncover homogeneous dense groups in a network. However, existing methods of k-core decomposition struggle to support in-depth exploration of homogeneous dense groups. To address this issue, we store social networks in a graph database, taking advantage of its characteristics such as property indexes and batch queries. Based on this storage, we propose a k-core decomposition algorithm to improve the efficiency of homogeneous dense group detection. Subsequently, we introduce a core member filtering algorithm for identifying core members, a key exploration goal of this study. In experiments, we verify the efficiency of the k-core decomposition algorithm. Finally, we conduct an in-depth analysis of the characteristics of k-cores and their core members, yielding several important conclusions. For example, the relationship between the core number and the number of nodes obeys the power law distribution. In addition, we find that despite the strong connection of the core members, they do not play an important role in the information spreading of social networks.