mlsanityData to 7 Oct 2026

Graph Capsule Convolutional Neural Networks

arXiv 1805.08090 ↗First posted 21 May 2018stat.ML cs.CV cs.LGcited by 27

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Abstract (arXiv)

Graph Convolutional Neural Networks (GCNNs) are the most recent exciting advancement in deep learning field and their applications are quickly spreading in multi-cross-domains including bioinformatics, chemoinformatics, social networks, natural language processing and computer vision. In this paper, we expose and tackle some of the basic weaknesses of a GCNN model with a capsule idea presented in citehinton2011transforming and propose our Graph Capsule Network (GCAPS-CNN) model. In addition, we design our GCAPS-CNN model to solve especially graph classification problem which current GCNN models find challenging. Through extensive experiments, we show that our proposed Graph Capsule Network can significantly outperforms both the existing state-of-art deep learning methods and graph kernels on graph classification benchmark datasets.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 May 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 8; 2023: 4; 2024: 8; 2025: 1; 2026: 2.

27 papers.

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  1. 2021SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanismcited by 75
  2. 2023CktGNN: Circuit Graph Neural Network for Electronic Design Automationcited by 55
  3. 2021DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendationcited by 28
  4. 2023The Expressive Power of Graph Neural Networks: A Surveycited by 27
  5. 2022A Variational Edge Partition Model for Supervised Graph Representation Learningcited by 6
  6. 2023State of the Art and Potentialities of Graph-level Learningcited by 6
  7. 2024Graph Reinforcement Learning for Power Grids: A Comprehensive Surveycited by 6
  8. 2022What Functions Can Graph Neural Networks Generate?cited by 5
  9. 2022Hierarchical Capsule Prediction Network for Marketing Campaigns Effectcited by 5
  10. 2022Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Poolingcited by 5
  11. 2023AGFormer: Efficient Graph Representation with Anchor-Graph Transformercited by 5
  12. 2024Survey of Graph Neural Network for Internet of Things and NextG Networkscited by 4
15 more
  1. 2024Retinal Vessel Segmentation with Deep Graph and Capsule Reasoningcited by 2
  2. 2021Revisiting 2D Convolutional Neural Networks for Graph-based Applicationscited by 1
  3. 2024Graph Learning-based Fleet Scheduling for Urban Air Mobility under Operational Constraints, Varying Demand & Uncertaintiescited by 1
  4. 2024Bigraph Matching Weighted with Learnt Incentive Function for Multi-Robot Task Allocationcited by 1
  5. 2024A Graph-based Adversarial Imitation Learning Framework for Reliable & Realtime Fleet Scheduling in Urban Air Mobilitycited by 1
  6. 2021Capsule Graph Neural Networks with EM Routingcited by 0
  7. 2022Distribution Preserving Graph Representation Learningcited by 0
  8. 2022Unified GCNs: Towards Connecting GCNs with CNNscited by 0
  9. 2022Graph-level Neural Networks: Current Progress and Future Directionscited by 0
  10. 2022A Multi-scale Graph Signature for Persistence Diagrams based on Return Probabilities of Random Walkscited by 0
  11. 2024Large-Language-Model Empowered Dose Volume Histogram Prediction for Intensity Modulated Radiotherapycited by 0
  12. 2024SPGNN: Recognizing Salient Subgraph Patterns via Enhanced Graph Convolution and Poolingcited by 0
  13. 2025Automated Generation of Diverse Courses of Actions for Multi-Agent Operations using Binary Optimization and Graph Learningcited by 0
  14. 2026Topology-Aware Reinforcement Learning over Graphs for Resilient Power Distribution Networkscited by 0
  15. 2026Designing Active Tether-Net Systems for Space Debris Capture with Graph-Learning-Aided Mixed-Combinatorial Optimizationcited by 0