mlsanityData to 7 Oct 2026

Spectral-based Graph Convolutional Network for Directed Graphs

arXiv 1907.08990 ↗First posted 21 Jul 2019cs.LG cs.SI stat.MLcited by 38

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Abstract (arXiv)

Graph convolutional networks(GCNs) have become the most popular approaches for graph data in these days because of their powerful ability to extract features from graph. GCNs approaches are divided into two categories, spectral-based and spatial-based. As the earliest convolutional networks for graph data, spectral-based GCNs have achieved impressive results in many graph related analytics tasks. However, spectral-based models cannot directly work on directed graphs. In this paper, we propose an improved spectral-based GCN for the directed graph by leveraging redefined Laplacians to improve its propagation model. Our approach can work directly on directed graph data in semi-supervised nodes classification tasks. Experiments on a number of directed graph datasets demonstrate that our approach outperforms the state-of-the-art methods.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jul 2019.

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Citing papers per year: 2021: 6; 2022: 10; 2023: 9; 2024: 8; 2025: 2; 2026: 3.

38 papers.

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  1. 2021MagNet: A Neural Network for Directed Graphscited by 104
  2. 2023Edge Directionality Improves Learning on Heterophilic Graphscited by 88
  3. 2022MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplaciancited by 28
  4. 2023Provably Powerful Graph Neural Networks for Directed Multigraphscited by 24
  5. 2022Transformers over Directed Acyclic Graphscited by 23
  6. 2022Directed Graph Auto-Encoderscited by 22
  7. 2022PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphscited by 18
  8. 2023High Fidelity 3D Hand Shape Reconstruction via Scalable Graph Frequency Decompositioncited by 18
  9. 2022Geometric Scattering on Measure Spacescited by 17
  10. 2021DIGRAC: Digraph Clustering Based on Flow Imbalancecited by 15
  11. 2022Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphscited by 15
  12. 2021WGCN: Graph Convolutional Networks with Weighted Structural Featurescited by 12
26 more
  1. 2022A Magnetic Framelet-Based Convolutional Neural Network for Directed Graphscited by 11
  2. 2021D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation Learningcited by 8
  3. 2023Graph Neural Networks based Log Anomaly Detection and Explanationcited by 8
  4. 2021MGC: A Complex-Valued Graph Convolutional Network for Directed Graphscited by 7
  5. 2022A Simple Yet Effective SVD-GCN for Directed Graphscited by 7
  6. 2024What Are Good Positional Encodings for Directed Graphs?cited by 7
  7. 2024Improving Graph Neural Networks by Learning Continuous Edge Directionscited by 3
  8. 2022A Graph Convolution for Signed Directed Graphscited by 2
  9. 2024Graph Neural Networks for Edge Signals: Orientation Equivariance and Invariancecited by 2
  10. 2024Towards Data-centric Machine Learning on Directed Graphs: a Surveycited by 2
  11. 2021Asymmetric Graph Representation Learningcited by 1
  12. 2023GRANDE: a neural model over directed multigraphs with application to anti-money launderingcited by 1
  13. 2023DEDGAT: Dual Embedding of Directed Graph Attention Networks for Detecting Financial Riskcited by 1
  14. 2023Can Directed Graph Neural Networks be Adversarially Robust?cited by 1
  15. 2023A Simple and Scalable Graph Neural Network for Large Directed Graphscited by 1
  16. 2024A Benchmark on Directed Graph Representation Learning in Hardware Designscited by 1
  17. 2024Scale-aware Message Passing For Graph Node Classificationcited by 1
  18. 2026LineMVGNN: Anti-Money Laundering with Line-Graph-Assisted Multi-View Graph Neural Networkscited by 1
  19. 2026Nonlinear Laplacians Improve Signed-Directed Graph Learningcited by 1
  20. 2022From Spectral Graph Convolutions to Large Scale Graph Convolutional Networkscited by 0
  21. 2023Dynamic Graph Neural Network with Adaptive Edge Attributes for Air Quality Predictionscited by 0
  22. 2024ScaleNet: Scale Invariance Learning in Directed Graphscited by 0
  23. 2024Introduction to Graph Neural Networks for Machine Learning Engineerscited by 0
  24. 2025Understanding the Design Principles of Link Prediction in Directed Settingscited by 0
  25. 2025Ignoring Directionality Leads to Compromised Graph Neural Network Explanationscited by 0
  26. 2026Cone Rayleigh Levels: Finite Perturbations and Certified Controlcited by 0