Influence-based community partition for social networks

Abstract

Background/Purpose: Community partition is of great importance in sociology, biology and computer science. Due to the exponentially increasing amount of social network applications, a fast and accurate method is necessary for community partition in social networks. In view of this, we investigate the social community partition problem from the perspective of influence propagation, which is one of the most important features of social communication. Methods: We formulate social community partition as a combinatorial optimization problem that aims at partitioning a social network into K disjoint communities such that the sum of influence propagation within each community is maximized. When K=2 we develop an optimal algorithm that has a provable performance guarantee for a class of influence propagation models. For general K, we prove that it is NP-hard to find a maximum partition for social networks in the well-known linear threshold and independent cascade models. To get near-optimal solutions, we develop a greedy algorithm based on the optimal algorithm. We also develop a heuristic algorithm with a low computational complexity for large social networks. Results: To evaluate the practical efficiency of our algorithms, we do a simulation study based on real world scenarios. The experiments are conducted on three real-world social networks, and the experimental results show that more accurate partitions according to influence propagation can be obtained using our algorithms rather than using some classic community partition algorithms. Conclusions: In this study, we investigate the community partition problem in social networks. It is formulated as an optimization problem and investigated both theoretically and practically. The results can be applied to find communities in social networks and are also useful for the influence propagation problem in social networks.