Deep graph alignment network

Abstract

Graph alignment, also known as network alignment has many applications in data mining tasks. It aims to find the node correspondence across disjoint graphs. Recently, various methods like representation learning methods, spectral methods have been proposed to solve the graph alignment problem, but they either only consider the local structure information but ignore the neighborhood similarity, or their alignment process is easy to be disturbed by nodes with similar structure or attribute. In this paper, we consider both center and neighborhood similarities, aiming to reduce the inconsistency between them and enlarge the difference among node representations. We further propose model DGAN(Deep Graph Alignment Network) containing the DNN module and GCN module to learn more unique node representations under the guidance of the attribute-supervised module. Moreover, we theoretically prove that most spectral methods can be unified into a linear GCN model. By extensive experiments on public benchmarks, we show that our model achieves a good balance between alignment accuracy and speed over multiple datasets compared with existing methods.