Extracting representative user subset of social networks towards user characteristics and topological features

Extracting a subset of representative users from the original set in social networks plays a critical role in Social Network Analysis. In existing studies, some researchers focus on preserving users’ characteristics when sampling representative users, while others pay attention to preserving the topology structure. However, both users’ characteristics and the network topology contain abundant information of users. Thus, it is critical to preserve both of them while extracting the representative user subset. To achieve the goal, we propose a novel approach in this study, and formulate the problem as RUS (Representative User Subset) problem that is proved as an NP-Hard problem. To solve RUS problem, we propose two approaches KS (K-Selected) and an optimized method (ACS) that are both consisted of a clustering algorithm and a sampling model, where a greedy heuristic algorithm is proposed to solve the sampling model. In addition, we propose the pruning strategy by taking advantage of MaxHeap structure. To validate the performance of the proposed approach, extensive experiments are conducted on two real-world datasets. Results demonstrate that our methods outperform state-of-the-art approaches.