mlsanityData to 7 Oct 2026

mGNN: Generalizing the Graph Neural Networks to the Multilayer Case

arXiv 2109.10119 ↗First posted 21 Sep 2021cs.LG physics.soc-phcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 25 of its 60 references that are in the map. First posted on arXiv 21 Sep 2021.

Builds on

25 of 60 references.

References · 25

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
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13 more
  1. 2016Random walks and diffusion on networkscited by 28
  2. 2020Utilising Graph Machine Learning within Drug Discovery and Developmentcited by 26
  3. 2019Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Povertycited by 19
  4. 2021Machine learning dismantling and early-warning signals of disintegration in complex systemscited by 14
  5. 2013Random Walks on Multiplex Networkscited by 10
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  7. 2013Eigenvector centrality of nodes in multiplex networkscited by 6
  8. 2013Structural measures for multiplex networkscited by 6
  9. 2015Evolutionary games on multilayer networks: A colloquiumcited by 5
  10. 2013Centrality in Interconnected Multilayer Networkscited by 4
  11. 2014Measuring and modelling correlations in multiplex networkscited by 2
  12. 2013Communicability reveals a transition to coordinated behavior in multiplex networkscited by 1
  13. 2018Deep Learning Super-Diffusion in Multiplex Networkscited by 1

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 1.

4 papers.

  1. 2024Attention-based Dynamic Multilayer Graph Neural Networks for Loan Default Predictioncited by 5
  2. 2024PAHD: Perception-Action based Human Decision Making using Explainable Graph Neural Networks on SAR Imagescited by 1
  3. 2023Learning the Right Layers: a Data-Driven Layer-Aggregation Strategy for Semi-Supervised Learning on Multilayer Graphscited by 0
  4. 2025Multilayer GNN for Predictive Maintenance and Clustering in Power Gridscited by 0