Accurate Network Alignment via Consistency in Node Evolution

Abstract

Network alignment, which integrates multiple network resources by identifying anchor nodes that exist in different networks, is beneficial for conducting comprehensive network analysis. Although there have been many studies on network alignment, most of them are limited to static scenarios and only can achieve acceptable top-$\alpha$ ($\alpha > 10$) results. In the absence of considering dynamic changes in networks, accurate network alignment (i.e., top-1 result) faces two problems: 1) Missing information: focusing solely on aligning networks at a specific time leads to low top-1 performance due to the lack of information from other time periods; 2) Confusing information: ignoring temporal information and focusing on aligning networks across the entire time span leads to low top-1 performance due to inability to distinguish the neighborhood nodes of anchor nodes. In this paper, we propose a dynamic network alignment method, which aims to achieve better top-1 alignment results with consider changing network structures over time. Towards this end, we learn the representations of nodes in the changing network structure with time, and preserve the consistency of anchor node pairs during the time-evolution process. First, we employ a Structure-Time-aware module to capture network dynamics while preserving network structure and learning node representations that incorporate temporal information. Second, we ensure the global and local consistency of anchor node pairs over time by utilizing linear and similarity functions, respectively. Finally, we determine whether two nodes are anchor node pairs by maintaining consistency between global, local, and node representations. Experimental results obtained from real-world datasets demonstrate that the proposed model achieves performance comparable to several state-of-the-art methods.