IMap: Incremental Node Mapping between Large Graphs Using GNN

Abstract

Node mapping between large graphs (or network alignment) plays a key preprocessing role in joint-graph data mining applications like social link prediction, cross-platform recommendation, etc. Most existing approaches attempt to perform alignment at the granularity of entire graphs, while handling the whole graphs may lower the scalability and the noisy nodes/edges in the graphs may impact the effectiveness. From the observation that potential node mappings always appear near known corresponding nodes, we propose iMAP, a novel sub-graph expansion based alignment framework to incrementally construct meaningful sub-graphs and perform alignment on each sub-graph pair iteratively, which reduces the unnecessary computation cost in the original raw networks and improves effectiveness via excluding possible noises. Specifically, iMap builds a candidate sub-graph around known matched nodes initially. In each following iteration, iMap trains an alignment model to infer the node mapping relationship between sub-graphs, from which the sub-graphs are further extended and refined. In addition, we design a Graph Neural Network(GNN) based model named MAP on each sub-graph pair in the iMap framework. MAP utilizes trainable Multi-layer Perception (MLP) prediction heads for similarity computation and employs a mixed loss function consisting of the ranking loss for contrastive learning and the cross-entropy loss for classification. Extensive experiments conducted on real social networks demonstrate superior efficiency and effectiveness (above 12% improvement) of our proposed method compared to several state-of-the-art methods.