mGNN: Generalizing the Graph Neural Networks to the Multilayer Case

2021/09/21 by Marco Grassia, Manlio De Domenico, Grassia, Marco +3 · 1 citation
Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Physical sciences #Graph Theory and Algorithms #Machine Learning (cs.LG) #Physics and Society (physics.soc-ph)

paper · pdf · doi:10.48550/arxiv.2109.10119

openalex publication_date 2021/09/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Networks are a powerful tool to model complex systems, and the definition of many Graph Neural Networks (GNN), Deep Learning algorithms that can handle networks, has opened a new way to approach many real-world problems that would be hardly or even untractable. In this paper, we propose mGNN, a framework meant to generalize GNNs to the case of multi-layer networks, i.e., networks that can model multiple kinds of interactions and relations between nodes. Our approach is general (i.e., not task specific) and has the advantage of extending any type of GNN without any computational overhead. We test the framework into three different tasks (node and network classification, link prediction) to validate it.

Citations

• Tensor Decompositions and Applications
• Structural reducibility of multilayer networks
• Semi-Supervised Classification with Graph Convolutional Networks
• Geometric Deep Learning: Going beyond Euclidean data
• Fast Graph Representation Learning with PyTorch Geometric
• Chip Placement with Deep Reinforcement Learning

Cited by

• Multilayer GNN for Predictive Maintenance and Clustering in Power Grids

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