MWTP: A heterogeneous multiplex representation learning framework for link prediction of weak ties.

Abstract

Weak ties that bridge different communities are crucial for preserving global connectivity, enhancing resilience, and maintaining functionality and dynamics of complex networks, However, making accurate link predictions for weak ties remain challenging due to lacking of common neighbors. Most complex systems, such as transportation and social networks, comprise multiple types of interactions, which can be modeled by multiplex networks with each layer representing a different type of connection. Better utilizing information from other layers can mitigate the lack of information for predicting weak ties. Here, we propose a GNN-based representation learning framework for Multiplex Weak Tie Prediction (MWTP). It leverages both an intra-layer and an inter-layer aggregator to effectively learn and fuse information across different layers. The intra-layer one integrates features from multi-order neighbors, and the inter-layer aggregation exploits either logit regression or a more sophisticated semantic voting mechanism to compute nodal-level inter-layer attentions, leading to two variants of our framework, MWTP-logit, and MWTP-semantic. The former one is more efficient in implementation attribute to fewer parameters, while the latter one is slower but has stronger learning capabilities. Extensive experiments demonstrate that our MWTPs outperform eleven popular baselines for predicting both weak ties and all ties across diverse real-world multiplex networks. Additionally, MWTPs achieve good prediction performance with a relatively small training size.