Frequent Pattern Mining in Big Social Graphs

Abstract

With the popularity of graph applications, frequent pattern mining (FPM) has been playing a significant role in many domains, such as social networks and bioinformatics. However, due to the exponential time complexity of FPM, it is a challenge for most existing techniques in big dense graphs, such as social graphs. In this paper, with the defined concept of social pattern, a corresponding linear time computable support calculation measurement, called Minimum Independent Individual based support, is proposed. Then, we adopt the concept of pathgraph to store the appearance of social patterns and propose a novel approach ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SocMi</small> ) to solve the problem of frequent social pattern mining. Additionally, in order to reduce the exponential time consumption to explore big graphs, an approximate approach ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ASocMi</small> ) with a quick exploration strategy is proposed. Moreover, the proposed approaches have been further optimized by using cache during processing. Finally, an extensive empirical study in real-world social graphs has demonstrated the effectiveness and efficiency of the proposed approaches compared with the state-of-the-art approaches.