mlsanityData to 14 Oct 2026

edGNN: a Simple and Powerful GNN for Directed Labeled Graphs

arXiv 1904.08745 ↗First posted 18 Apr 2019cs.LG stat.MLcited by 18

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Abstract (arXiv)

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.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Apr 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 3; 2023: 2; 2024: 5; 2025: 1; 2026: 4.

18 papers.

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  1. 2023Edge Directionality Improves Learning on Heterophilic Graphscited by 90
  2. 2021Hierarchical Graph Representations in Digital Pathologycited by 59
  3. 2023Provably Powerful Graph Neural Networks for Directed Multigraphscited by 27
  4. 2022Directed Graph Auto-Encoderscited by 22
  5. 2022Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approachcited by 8
  6. 2024What Are Good Positional Encodings for Directed Graphs?cited by 8
  7. 2022Two-level Graph Neural Networkcited by 4
  8. 2024Explainable Molecular Property Prediction: Aligning Chemical Concepts with Predictions via Language Modelscited by 3
  9. 2021GCNScheduler: Scheduling Distributed Computing Applications using Graph Convolutional Networkscited by 2
  10. 2024Towards Data-centric Machine Learning on Directed Graphs: a Surveycited by 2
  11. 2021Revisiting 2D Convolutional Neural Networks for Graph-based Applicationscited by 1
  12. 2024Manufacturing Service Capability Prediction with Graph Neural Networkscited by 1
6 more
  1. 2024A Benchmark on Directed Graph Representation Learning in Hardware Designscited by 1
  2. 2025Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directionscited by 1
  3. 2026A Tight Expressivity Hierarchy for GNN-Based Entity Resolution in Master Data Managementcited by 0
  4. 2026Extracting Money Laundering Transactions from Quasi-Temporal Graph Representationcited by 0
  5. 2026Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networkscited by 0
  6. 2026Path Laplacian Encodings for Directed Graphscited by 0