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edGNN: a Simple and Powerful GNN for Directed Labeled Graphs

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arxiv 1904.08745 v2 pith:XDDZ3STF submitted 2019-04-18 cs.LG stat.ML

Guillaume Jaume , An-phi Nguyen , María Rodríguez Martínez , Jean-Philippe Thiran , Maria Gabrani This is my paper · ORCID
classification cs.LGstat.ML
keywords graphdirectededgnngraphsbuildinglabeledlabelsneural
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The ability of a graph neural network (GNN) to leverage both the graph topology and graph labels is fundamental to building discriminative node and graph embeddings. Building on previous work, we theoretically show that edGNN, our model for directed labeled graphs, is as powerful as the Weisfeiler-Lehman algorithm for graph isomorphism. Our experiments support our theoretical findings, confirming that graph neural networks can be used effectively for inference problems on directed graphs with both node and edge labels. Code available at https://github.com/guillaumejaume/edGNN.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 11 citations worldwide. Full citation record

  1. Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations

    cs.LG 2024-11 conditional novelty 6.0 of 10

    MEGA-GNN adds a parallel-edge aggregation stage inside each message passing layer, giving multigraph GNNs permutation equivariance and, when edge features are totally ordered, universality, with up to 13 percentage po...

  2. Towards Data-centric Machine Learning on Directed Graphs: a Survey

    cs.LG 2024-11 unverdicted novelty 3.0 of 10

    A survey taxonomizing directed graph neural networks into message-passing, eigenpolynomial, and sequence-based frameworks and re-reading them from a data-centric perspective.

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