mlsanityData to 7 Oct 2026

MotifNet: a motif-based Graph Convolutional Network for directed graphs

arXiv 1802.01572 ↗First posted 4 Feb 2018cs.LGcited by 63

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Abstract (arXiv)

Deep learning on graphs and in particular, graph convolutional neural networks, have recently attracted significant attention in the machine learning community. Many of such techniques explore the analogy between the graph Laplacian eigenvectors and the classical Fourier basis, allowing to formulate the convolution as a multiplication in the spectral domain. One of the key drawback of spectral CNNs is their explicit assumption of an undirected graph, leading to a symmetric Laplacian matrix with orthogonal eigendecomposition. In this work we propose MotifNet, a graph CNN capable of dealing with directed graphs by exploiting local graph motifs. We present experimental evidence showing the advantage of our approach on real data.

Key facts. Cited by 63 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Feb 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 13; 2022: 18; 2023: 13; 2024: 10; 2025: 4; 2026: 5.

63 papers.

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  1. 2021Understanding over-squashing and bottlenecks on graphs via curvaturecited by 471
  2. 2021From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awarenesscited by 154
  3. 2021MagNet: A Neural Network for Directed Graphscited by 104
  4. 2023Edge Directionality Improves Learning on Heterophilic Graphscited by 88
  5. 2021SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanismcited by 75
  6. 2022Theory of Graph Neural Networks: Representation and Learningcited by 60
  7. 2021Property-Aware Relation Networks for Few-Shot Molecular Property Predictioncited by 41
  8. 2022PaSca: a Graph Neural Architecture Search System under the Scalable Paradigmcited by 37
  9. 2022MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplaciancited by 28
  10. 2021Topological Relational Learning on Graphscited by 27
  11. 2023TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discoverycited by 27
  12. 2023From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyondcited by 26
38 more
  1. 2021ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Networkcited by 24
  2. 2022Make Heterophily Graphs Better Fit GNN: A Graph Rewiring Approachcited by 23
  3. 2022Transformers over Directed Acyclic Graphscited by 23
  4. 2022Directed Graph Auto-Encoderscited by 22
  5. 2022Geodesic Graph Neural Network for Efficient Graph Representation Learningcited by 19
  6. 2023On Structural Expressive Power of Graph Transformerscited by 19
  7. 2021DIGRAC: Digraph Clustering Based on Flow Imbalancecited by 15
  8. 2022Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphscited by 15
  9. 2023Learning to Count Isomorphisms with Graph Neural Networkscited by 13
  10. 2022An Explainer for Temporal Graph Neural Networkscited by 12
  11. 2022NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learningcited by 11
  12. 2022MM-GNN: Mix-Moment Graph Neural Network towards Modeling Neighborhood Feature Distributioncited by 11
  13. 2022Toward Robust Graph Semi-Supervised Learning against Extreme Data Scarcitycited by 11
  14. 2022A Magnetic Framelet-Based Convolutional Neural Network for Directed Graphscited by 11
  15. 2023MAG-GNN: Reinforcement Learning Boosted Graph Neural Networkcited by 11
  16. 2021Motif Graph Neural Networkcited by 10
  17. 2023Graph Neural Networks in Vision-Language Image Understanding: A Surveycited by 10
  18. 2023Graph Neural Networks based Log Anomaly Detection and Explanationcited by 8
  19. 2022A Simple Yet Effective SVD-GCN for Directed Graphscited by 7
  20. 2023Learning Dynamic Graph Embeddings with Neural Controlled Differential Equationscited by 7
  21. 2024What Are Good Positional Encodings for Directed Graphs?cited by 7
  22. 2022Progressive Cross-modal Knowledge Distillation for Human Action Recognitioncited by 6
  23. 2021GMLP: Building Scalable and Flexible Graph Neural Networks with Feature-Message Passingcited by 5
  24. 2023Graph learning in robotics: a surveycited by 5
  25. 2022Empowering GNNs via Edge-Aware Weisfeiler-Leman Algorithmcited by 4
  26. 2024EMP: Effective Multidimensional Persistence for Graph Representation Learningcited by 4
  27. 2024Demystifying Higher-Order Graph Neural Networkscited by 4
  28. 2025Towards graph neural networks for provably solving convex optimization problemscited by 4
  29. 2022Towards Higher-order Topological Consistency for Unsupervised Network Alignmentcited by 3
  30. 2023MPool: Motif-Based Graph Poolingcited by 3
  31. 2024Graph Neural Networks for Edge Signals: Orientation Equivariance and Invariancecited by 2
  32. 2024Towards Data-centric Machine Learning on Directed Graphs: a Surveycited by 2
  33. 2026Retrieving Minimal and Sufficient Reasoning Subgraphs with Graph Foundation Models for Path-aware GraphRAGcited by 2
  34. 2021Asymmetric Graph Representation Learningcited by 1
  35. 2023Can Directed Graph Neural Networks be Adversarially Robust?cited by 1
  36. 2023Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustnesscited by 1
  37. 2024Inference of Sequential Patterns for Neural Message Passing in Temporal Graphscited by 1
  38. 2024A Benchmark on Directed Graph Representation Learning in Hardware Designscited by 1

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