Attention-based multi-layer network representation learning framework for network alignment

Abstract

Network alignment, which aims at finding the node correspondences between networks, is the cornerstone of multi-network applications. Existing efforts on network alignment suffer from the alignment space misregistration problem (i.e., the alignment spaces of two networks are not matched) and the alignment inconsistency problem (i.e., the consistency assumptions they held cannot be satisfied). To tackle these problems, in this paper, we propose an Attention-based Multi-layer Network representation learning framework for network alignment, named AMN. Specifically, to tackle alignment space misregistration problem, a novel network fusion strategy is proposed. It can establish connections between networks while preserving the specific information in each network. Based on this strategy, two networks are learned simultaneously and the representation spaces of them are matched. Secondly, an attention-based multi-layer graph neural network named A-GNN is devised, in which an innovative inter-layer attention mechanism is proposed. Different from existing attention mechanisms, the proposed inter-layer attention mechanism learns vector weights, so that it can fine-tune the consistent information in each dimension. Hence, AMN can make full use of the consistent information and alleviate the influence of alignment inconsistency problem. Experiments conducted on 4 kinds of real-world datasets show that AMN outperforms 9 state-of-the-art methods by at least 0.007–0.671 in terms of precision@1.