Scalable Heterogeneous Social Network Alignment through Synergistic Graph Partition

Abstract

Social network alignment has been an important research problem for social network analysis in recent years. With the identified shared users across networks, it will provide researchers with the opportunity to achieve a more comprehensive understanding of users' social activities both within and across networks. Social network alignment is a very difficult problem. Besides the challenges introduced by the network heterogeneity, the network alignment can be reduced to a combinatorial optimization problem with an extremely large search space. The learning effectiveness and efficiency of existing alignment models will be degraded significantly as the network size increases. In this paper, we focus on studying the scalable heterogeneous social network alignment problem, and propose to address it with a novel two-stage network alignment model, namely Scalable Heterogeneous Network Alignment (SHNA). Based on a group of intra-and inter-network meta diagrams, SHNA first partitions the social networks into a group of sub-networks synergistically. Via the partially known anchor links, SHNA can extract the partitioned sub-network correspondence relationships. Instead of aligning the complete input network, SHNA proposes to identify the anchor links between the matched subnetwork pairs, while those between the unmatched sub-networks will be pruned to effectively shrink the search space. Extensive experiments have been done to compare SHNA with the state-ofthe-art baseline methods on a real-world aligned social networks dataset. The experimental results have demonstrated both the effectiveness and efficiency of SHNA in addressing the problem.

From One to All: Learning to Match Heterogeneous and Partially Overlapped Graphs

“…Matching heterogeneous Graphs. Many existing methods for matching heterogeneous graphs are supervised (Wu et al 2019;Ren, Meng, and Zhang 2020;Sun et al 2020…”