Pay Attention to Weak Ties: A Heterogeneous Multiplex Representation Learning Framework for Link Prediction

TL;DR

This paper introduces MRGNN, a graph neural network framework designed to improve link prediction in multiplex networks, especially for weak ties connecting different communities, by adaptively aggregating multi-layer information.

Contribution

The paper proposes a novel multiplex GNN model that effectively captures inter-layer relations and enhances weak tie prediction in complex networks.

Findings

MRGNN outperforms existing algorithms on four multiplex networks.

The model significantly improves weak tie prediction accuracy.

Extensive experiments validate the effectiveness of the proposed framework.

Abstract

Graph neural networks (GNNs) can learn effective node representations that significantly improve link prediction accuracy. However, most GNN-based link prediction algorithms are incompetent to predict weak ties connecting different communities. Most link prediction algorithms are designed for networks with only one type of relation between nodes but neglect the fact that many complex systems, including transportation and social networks, consisting of multi-modalities of interactions that correspond to different nature of interactions and dynamics that can be modeled as multiplex network, where different types of relation are represented in different layers. This paper proposes a Multi-Relations-aware Graph Neural Network (MRGNN) framework to learn effective node representations for multiplex networks and make more accurate link predictions, especially for weak ties. Specifically, our model utilizes an intra-layer node-level feature propagation process and an inter-layer representation merge process, which applies a simple yet effective logistic or semantic attention voting mechanism to adaptively aggregate information from different layers. Extensive experiments on four diversified multiplex networks show that MRGNN outperforms the state-of-the-art multiplex link prediction algorithms on overall prediction accuracy, and works pretty well on forecasting weak ties