Evolution pattern mining on dynamic social network

Recently, due to the popularity of social websites and apps, considerable attention has been paid to the analysis of the structure of social networks. Clearly, social networks usually evolve over time; some new users and relationships are established; and some obsolete ones are removed. This dynamic feature definitely increases the complexity of pattern discovery. In this paper, we introduce a new representation to express the dynamic social network and a new type of pattern, the evolution pattern, to capture the interaction evolutions in a dynamic social network. Furthermore, a novel algorithm, evolution pattern miner (EPMiner), is developed to efficiently discover the evolution characteristics. EPMiner also employs some pruning strategies to effectively reduce the search space to improve the performance. The experimental results on several datasets show the efficiency and the scalability of EPMiner for extracting interaction evolution in dynamic networks. Finally, we apply EPMiner on real datasets to show the practicability of evolution pattern mining.

Automated summary

This study introduces a new representation for dynamic social networks and proposes a new type of pattern - evolution pattern. The development of an evolution pattern miner (EPMiner) is aimed at efficiently discovering evolution characteristics in dynamic networks. Experimental results demonstrate the efficiency and scalability of EPMiner in extracting interaction evolution, with practical applicability shown on real datasets.