mGNN: Generalizing the Graph Neural Networks to the Multilayer Case
arXiv:2109.10119
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.
Submitted to the IEEE Computer Society
References in corpus (6)
- Semi-Supervised Classification with Graph Convolutional Networks
- Fast Graph Representation Learning with PyTorch Geometric
- Diffusion dynamics on multiplex networks
- Layer aggregation and reducibility of multilayer interconnected networks
- Modeling the Multi-layer Nature of the European Air Transport Network: Resilience and Passengers Re-scheduling under random failures
- Chip Placement with Deep Reinforcement Learning