MSGCN: Multiplex Spatial Graph Convolution Network for Interlayer Link Weight Prediction

2025-04-24

AI Summary

This paper addresses the challenging problem of cross-layer link weight prediction in multilayer networks. We propose the Multi-Path Spatial Graph Convolutional Network (MSGCN), the first spatial-domain graph convolutional architecture generalized to multilayer (multiplex) networks. MSGCN jointly models topological dependencies and geometric relationships across layers through cross-layer adjacency aggregation and geometric structure embedding, enabling end-to-end regression for continuous link weights. Extensive experiments on multiple real-world multilayer networks demonstrate that MSGCN significantly outperforms existing binary link prediction and regression baselines. It achieves state-of-the-art performance in robustness, prediction accuracy, and generalization capability. By unifying structural modeling and continuous relational inference within a principled deep learning framework, MSGCN establishes a novel paradigm for multilayer network analysis.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Graph Neural Networks (GNNs) have been widely used for various learning tasks, ranging from node classification to link prediction. They have demonstrated excellent performance in multiple domains involving graph-structured data. However, an important category of learning tasks, namely link weight prediction, has received less emphasis due to its increased complexity compared to binary link classification. Link weight prediction becomes even more challenging when considering multilayer networks, where nodes can be interconnected across multiple layers. To address these challenges, we propose a new method named Multiplex Spatial Graph Convolution Network (MSGCN), which spatially embeds information across multiple layers to predict interlayer link weights. The MSGCN model generalizes spatial graph convolution to multiplex networks and captures the geometric structure of nodes across multiple layers. Extensive experiments using data with known interlayer link information show that the MSGCN model has robust, accurate, and generalizable link weight prediction performance across a wide variety of multiplex network structures.

Problem

• Predicts interlayer link weights in multilayer networks
• Generalizes spatial graph convolution for multiplex networks
• Addresses complexity of link weight versus binary prediction

Innovation

• Spatial embedding across multiple network layers
• Generalizes spatial graph convolution for multiplex networks
• Captures geometric structure of nodes interlayer