Contrastive Multi-View Multiplex Network Embedding with Applications to Robust Network Alignment

Abstract

Despite its success in learning network node representations, network embedding is still relatively new for multiplex networks (MNs) with multiple types of edges. In such networks, the inter-layer anchor links are usually missing, which represent the alignment relations between nodes on different layers and are a crucial prerequisite for many cross-network applications like network alignment. For mining such anchor links between layers for MNs, multiplex network embedding (MNE) has become one of the most promising techniques. In this paper, we consider two problems for MNs: 1) edges can be missing to different extent, and data augmentation may mitigate this issue; 2) the known alignment anchor links between layers can be misleading since the behaviors of nodes on different layers are not always consistent, so the most informative ones should be emphasized compared with those misleading ones. However, most existing works neglect the two problems and simply 1) adopt one structural view for all the layers (e.g. random walk with the same window size) and 2) equally extract information from all the anchor links. We propose an end-to-end contrastive framework called cM 2 NE for MNE, utilizing multiple structural views for each layer and learning with several plug-in components for different scenarios. Through end-to-end optimization on three levels, the intra-view, inter-view, and inter-layer level, our framework achieves to select the fitted views for different layers and maximize the inter-layer mutual information by emphasizing those most informative anchor links. Extensive experimental results on real-world datasets for node classification and multi-network alignment show that our approach consistently outperforms peer methods. CCS CONCEPTS• Information systems → Data mining.

Co-MLHAN: contrastive learning for multilayer heterogeneous attributed networks

“…Similar to Co-MLHAN, the views of this approach does not rely on changing the graph structure, but the similarity computation still employs a corruption of the attribute matrix, in contrast to our proposed approach. cM2NE (Xiong et al 2021) proposes a contrastive learning based embedding framework modeling multiple structural views for each layer. The contrastive learning is performed to extract information for a specific view, across the views of a layer and across the aligned layers.…”