mlsanityData to 7 Oct 2026

SPAGAN: Shortest Path Graph Attention Network

arXiv 2101.03464 ↗First posted 10 Jan 2021cs.LG cs.AIcited by 28

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Abstract (arXiv)

Graph convolutional networks (GCN) have recently demonstrated their potential in analyzing non-grid structure data that can be represented as graphs. The core idea is to encode the local topology of a graph, via convolutions, into the feature of a center node. In this paper, we propose a novel GCN model, which we term as Shortest Path Graph Attention Network (SPAGAN). Unlike conventional GCN models that carry out node-based attentions within each layer, the proposed SPAGAN conducts path-based attention that explicitly accounts for the influence of a sequence of nodes yielding the minimum cost, or shortest path, between the center node and its higher-order neighbors. SPAGAN therefore allows for a more informative and intact exploration of the graph structure and further {a} more effective aggregation of information from distant neighbors into the center node, as compared to node-based GCN methods. We test SPAGAN on the downstream classification task on several standard datasets, and achieve performances superior to the state of the art. Code is publicly available at https://github.com/ihollywhy/SPAGAN.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). Builds on 19 of its 31 references that are in the map. First posted on arXiv 10 Jan 2021.

Builds on

19 of 31 references.

References · 19

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  5. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  6. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  7. 2018Deep Graph Infomaxcited by 1,101
  8. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,097
  9. 2015Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)cited by 1,061
  10. 2018Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networkscited by 878
  11. 2016Geometric deep learning on graphs and manifolds using mixture model CNNscited by 468
  12. 2017Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphscited by 307
7 more
  1. 2019Semantic Graph Convolutional Networks for 3D Human Pose Regressioncited by 204
  2. 2018GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphscited by 164
  3. 2017SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernelscited by 160
  4. 2018Adaptive Graph Convolutional Neural Networkscited by 155
  5. 2018GeniePath: Graph Neural Networks with Adaptive Receptive Pathscited by 66
  6. 2018N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classificationcited by 54
  7. 2018Graph Convolutional Neural Networks via Motif-based Attentioncited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 9; 2023: 5; 2024: 5; 2025: 1; 2026: 2.

28 papers.

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  1. 2021Do Transformers Really Perform Bad for Graph Representation?cited by 318
  2. 2021Parameter Prediction for Unseen Deep Architecturescited by 89
  3. 2022Shortest Path Networks for Graph Property Predictioncited by 46
  4. 2021Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Trackingcited by 37
  5. 2022Rewiring with Positional Encodings for Graph Neural Networkscited by 35
  6. 2022Learning Graph Neural Networks for Image Style Transfercited by 29
  7. 2021Topological Relational Learning on Graphscited by 27
  8. 2021Meta-Aggregator: Learning to Aggregate for 1-bit Graph Neural Networkscited by 24
  9. 2022Path-Aware Graph Attention for HD Maps in Motion Predictioncited by 23
  10. 2023Deep Graph Reprogrammingcited by 22
  11. 2023Segment Anything in Non-Euclidean Domains: Challenges and Opportunitiescited by 16
  12. 2024TopoGCL: Topological Graph Contrastive Learningcited by 13
16 more
  1. 2023PathMLP: Smooth Path Towards High-order Homophilycited by 8
  2. 2022Hamilton-Jacobi equations on graphs with applications to semi-supervised learning and data depthcited by 7
  3. 2023On the Two Sides of Redundancy in Graph Neural Networkscited by 4
  4. 2024EMP: Effective Multidimensional Persistence for Graph Representation Learningcited by 4
  5. 2024DiRW: Path-Aware Digraph Learning for Heterophilycited by 3
  6. 2021Relational Graph Neural Network Design via Progressive Neural Architecture Searchcited by 1
  7. 2022Open World Learning Graph Convolution for Latency Estimation in Routing Networkscited by 1
  8. 2022Semantic Graph Neural Network with Multi-measure Learning for Semi-supervised Classificationcited by 1
  9. 2023Schema Inference for Interpretable Image Classificationcited by 1
  10. 2026Attention-based graph neural networks: a surveycited by 1
  11. 2022PathSAGE: Spatial Graph Attention Neural Networks With Random Path Samplingcited by 0
  12. 2022tBDFS: Temporal Graph Neural Network Leveraging DFScited by 0
  13. 2024Focus Where It Matters: Graph Selective State Focused Attention Networkscited by 0
  14. 2024HoGA: Higher-Order Graph Attention via Diversity-Aware k-Hop Samplingcited by 0
  15. 2025Large Language Model Evaluated Stand-alone Attention-Assisted Graph Neural Network with Spatial and Structural Information Interaction for Precise Endoscopic Image Segmentationcited by 0
  16. 2026Pro-ZD: A Transferable Graph Neural Network Approach for Proactive Zero-Day Threats Mitigationcited by 0