Hierarchical Structure Of Community Members Detection Techniques And Applications In Social Networks

Community structure is one of the most important structure for social network analysis. Community has varied sizes under different resolutions, and the members play different roles inside the community. Current methods often predefine fixed roles of members and only detect fixed hierarchy structures that are not consistent with real-world communities; methods with hand-crafted thresholds bring difficulties in real applications. In order to solve the limitations above, we introduce a novel structure to dig finer information by partitioning the members into several levels according to their belonging coefficients. We call this novel structure as Hierarchical Structure of Members(HSM) and discuss its properties in continuity, comparability, consistency and stability which reveal the multi-resolution of community as well as the intra-relations among members. In order to detect the HSM, we introduce a detection framework of hierarchical structure of members under the framework of community detection based on seeds. To improve the diversity of seed communities, we propose a seed detection method based on link pattern to detect both clique structures and star structures in the network. Under this framework, we propose a two-phrase method, Random Walk and Linear Regression(RWLR), to detect HSM. The method measures the belonging coefficients of members by random walk and then divides the members into multiple segments by linear regression. Experiments show that members in the same level hold the same properties and HSM reveals multi-resolution of community. Besides, the comparison in benchmarks shows the efficiency in community detection. Finally, we apply HSM to analyze social networks, including visualization of community structures in large social networks and interactive recommendations in Amazon network.