FINAL: Fast Attributed Network Alignment
Si Zhang*, Arizona State University; Hanghang Tong, Arizona State University
Multiple networks naturally appear in numerous high-impact applications. Network alignment (i.e., ﬁnding the node correspondence across diﬀerent networks) is often the very ﬁrst step for many data mining tasks. Most, if not all, of the existing alignment methods are solely based on the topology of the underlying networks. Nonetheless, many real networks often have rich at-tribute information on nodes and/or edges. In this paper, we propose a family of algorithms (FINAL) to align attributed networks. The key idea is to leverage the node/edge attribute information to guide (topology-based) alignment process. We formulate this problem from an optimization perspective based on the alignment consistency principle, and develop eﬀective and scalable algorithms to solve it. Our experiments on real networks show that (1) by leveraging the attribute information, our algorithms can signiﬁcantly improve the alignment accuracy (i.e., up to a 30% improvement over the existing methods); (2) compared with the exact solution, our proposed fast alignment algorithm leads to a more than 10× speed-up, while preserving a 95% ac-curacy; and (3) our on-query alignment method scales linearly, with an around 90% ranking accuracy compared with our exact full alignment method and a near real-time response time.