Dual Graph Convolutional Networks for Social Network Alignment

Social network alignment aims to discover the potential correspondence between users across different social platforms. Recent advances in graph representation learning have brought a new upsurge to network alignment. Most existing representation-based methods extract local structural information of social networks from users' neighborhoods, but the global structural information has not been fully exploited. Therefore, this manuscript proposes a dual graph convolutional networks-based method (DualNA) for social network alignment, which combines user representation learning and user alignment in a unified framework. Specifically, we design dual graph convolutional networks as feature extractors to capture the local and global structural information of social networks, and apply a two-part constraint mechanism, including reconstruction loss and contrastive loss, to jointly optimize the graph representation learning process. As a result, the learned user representations can not only preserve the local and global features of original networks, but also be distinguishable and suitable for the downstream task of social network alignment. Extensive experiments on three real-world datasets show that our proposed method outperforms all baselines. The ablation studies further illustrate the rationality and effectiveness of our method.