Large-Scale Graph Alignment Based on Topological Structure Representation Learning

Graph data has attracted lots of attention due to its strong ability of representing complex relationships. It has been widely used in many fields, such as social networks, academic cooperation, road traffic, and biological information. Graph alignment aims to find node pairs belonging to the same entity in different graphs, which have valuable applications in many fields. For example, associating accounts belonging to the same user in different social networks can provide richer user behavior profiles for recommender systems, and aligning protein networks of different biological tissues can assist researchers in analyzing the characteristics and functions of proteins. However, unsupervised graph alignment using the topological information of graphs has always been one of the important problems faced by graph data mining in the absence of manual annotation information. There are difficulties in finding initial seed nodes and low computational efficiency, especially for large-scale graph alignment tasks. To solve these problems, this paper proposes a large-scale unsupervised graph alignment framework based on topological structure representation learning. Firstly, a typical subgraph is selected from each of the graphs as a candidate set of seed nodes. The local topological information is used to retrieve a set of highly reliable seed node pairs. Then, we use the seed nodes to fuse the matching graphs, and propose an efficient unsupervised representation learning algorithm to map the fused graph into a unified vector space. Finally, large-scale graph alignment is realized based on the learned node vectors. Compared with existing methods, the proposed approach uses the least time in large-scale graph alignment tasks and achieves the best performance of alignment accuracy. Moreover, the structural differences of graphs have limited impacts on the performance of the proposed method.