Papers › edGNN: a Simple and Powerful GNN for Directed Labeled Graphs
edGNN: a Simple and Powerful GNN for Directed Labeled Graphs
Guillaume Jaume, An-phi Nguyen, María Rodríguez Martínez, Jean-Philippe Thiran, Maria Gabrani
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Graph Classification | MUTAG | edGNN (max) | Accuracy | 88.8% | #33 of 74 | Archive leaderboard | report |
| Graph Classification | MUTAG | edGNN (avg) | Accuracy | 86.9% | #51 of 74 | Archive leaderboard | report |
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Methods
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