Collaborative Cross-Network Embedding Framework for Network Alignment

Haifeng Zhang, Guojing Ren, Xiao Ding, Li Zhou, Xing-yi Zhang

Abstract: Network alignment aims to identify the corresponding nodes belonging to the same entity across different networks, which is a fundamental task in various applications. Existing embedding-based approaches usually involve two stages, namely embedding and matching. The embedding stage conducts network embedding on each network to capture the primary structural regularity. In the matching stage, a mapping function is built to project the learned embeddings to the same latent space. However, these approaches typically encounter two challenges: (1) the difficulty of unifying the elusive embedding spaces to the same latent space; (2) the difficulty of distinguishing the real anchor nodes from their neighbors, resulting in the confounding matching problem. To address these challenges, we present the Collaborative Cross-Network Embedding (CCNE) framework in this paper. This framework provides a collaborative and straightforward paradigm to better unify the two networks to the same latent space by preserving both intra- and inter-network structural features, without the need for a carefully designed mapping function. Meanwhile, a hard negative sampling strategy is adopted to distinguish anchor nodes from their sampled neighbors. Furthermore, an iterative CCNE is proposed to alleviate the scarcity of observed anchor links. Extensive experiments on real social networks demonstrate that the proposed collaborative framework outperforms current embedding-matching methods in terms of accuracy, robustness as well as compatibility.