Collaborative partitioning for multiple social networks with anchor nodes

Abstract

Plenty of individuals are getting involved in more than one social networks, and maintaining multiple relationships of social networks. The value behind the integrated information of multiple social networks is high. Howerver, the research of multiple social networks has been less studied. Our work presented in this paper taps into abundant information of multiple social networks and aims to resolve the initial phase problem of multi-related social network analysis based on MapReduce by partition the mutli-related social networks into non-intersecting subsets. To concretize our discussion, we propose a new multilevel framework (CPMN), which usually proceed in four stages, Merging Phase, Coarsening Phase, Intial Partitioning Phase and Uncoarsening Phase. We propose a modified matching strategy in the second stage and a modified refinement algorithm in the fourth stage. We prove the effective of CPMN on both synthetic data and real datasets. Experiments show that the same node in different social networks is assigned to the same partition by 100% without sacrificing the load balance and edgecut too much. We believe that our work will shed light on the study of multiple social networks based on MapReduce.