# edGNN: a Simple and Powerful GNN for Directed Labeled Graphs

arXiv: https://arxiv.org/abs/1904.08745 · HTML: https://mlsanity.com/paper/1904.08745/

## Key facts

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

## Built on by (most cited first, 18 of 18)

- [Edge Directionality Improves Learning on Heterophilic Graphs](https://mlsanity.com/paper/2305.10498/) (2023)
- [Hierarchical Graph Representations in Digital Pathology](https://mlsanity.com/paper/2102.11057/) (2021)
- [Provably Powerful Graph Neural Networks for Directed Multigraphs](https://mlsanity.com/paper/2306.11586/) (2023)
- [Directed Graph Auto-Encoders](https://mlsanity.com/paper/2202.12449/) (2022)
- [Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approach](https://mlsanity.com/paper/2201.05158/) (2022)
- [What Are Good Positional Encodings for Directed Graphs?](https://mlsanity.com/paper/2407.20912/) (2024)
- [Two-level Graph Neural Network](https://mlsanity.com/paper/2201.01190/) (2022)
- [Explainable Molecular Property Prediction: Aligning Chemical Concepts with Predictions via Language Models](https://mlsanity.com/paper/2405.16041/) (2024)
- [GCNScheduler: Scheduling Distributed Computing Applications using Graph Convolutional Networks](https://mlsanity.com/paper/2110.11552/) (2021)
- [Towards Data-centric Machine Learning on Directed Graphs: a Survey](https://mlsanity.com/paper/2412.01849/) (2024)
- [Revisiting 2D Convolutional Neural Networks for Graph-based Applications](https://mlsanity.com/paper/2105.11016/) (2021)
- [Manufacturing Service Capability Prediction with Graph Neural Networks](https://mlsanity.com/paper/2403.17239/) (2024)
- [A Benchmark on Directed Graph Representation Learning in Hardware Designs](https://mlsanity.com/paper/2410.06460/) (2024)
- [Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions](https://mlsanity.com/paper/2503.10058/) (2025)
- [A Tight Expressivity Hierarchy for GNN-Based Entity Resolution in Master Data Management](https://mlsanity.com/paper/2603.27154/) (2026)
- [Extracting Money Laundering Transactions from Quasi-Temporal Graph Representation](https://mlsanity.com/paper/2604.02899/) (2026)
- [Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks](https://mlsanity.com/paper/2605.26243/) (2026)
- [Path Laplacian Encodings for Directed Graphs](https://mlsanity.com/paper/2610.04657/) (2026)

## 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.