Deep graph alignment network

Wei Tang, Jingyu Wang, Qi Qi, Haifeng Sun, Shimin Tao, Hao Yang

Abstract: Highlights•We mathematically proved that several spectral alignment methods (NMF, IsoRank and FINAL) can be unified into a general heuristic form. From representation view, we further prove the heuristic is equal to a linear GCN framework without inserting self-loops.•We theoretically illustrate that there exist issues about inconsistency and attribute difference when GCN is directly applied to graph alignment. Hence, we further propose a novel Deep Graph Alignment Network (DGAN) which can effectively address this issue.•We conduct extensive experiments on public benchmarks and the experimental results show our framework notably outperforms traditional graph alignment methods by a large margin.