# Graph Capsule Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1805.08090 · HTML: https://mlsanity.com/paper/1805.08090/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2018.

## Built on by (most cited first, 27 of 27)

- [SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism](https://mlsanity.com/paper/2101.08170/) (2021)
- [CktGNN: Circuit Graph Neural Network for Electronic Design Automation](https://mlsanity.com/paper/2308.16406/) (2023)
- [DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation](https://mlsanity.com/paper/2106.10879/) (2021)
- [The Expressive Power of Graph Neural Networks: A Survey](https://mlsanity.com/paper/2308.08235/) (2023)
- [Graph Reinforcement Learning for Power Grids: A Comprehensive Survey](https://mlsanity.com/paper/2407.04522/) (2024)
- [A Variational Edge Partition Model for Supervised Graph Representation Learning](https://mlsanity.com/paper/2202.03233/) (2022)
- [State of the Art and Potentialities of Graph-level Learning](https://mlsanity.com/paper/2301.05860/) (2023)
- [What Functions Can Graph Neural Networks Generate?](https://mlsanity.com/paper/2202.08833/) (2022)
- [Hierarchical Capsule Prediction Network for Marketing Campaigns Effect](https://mlsanity.com/paper/2208.10113/) (2022)
- [Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Pooling](https://mlsanity.com/paper/2209.09245/) (2022)
- [AGFormer: Efficient Graph Representation with Anchor-Graph Transformer](https://mlsanity.com/paper/2305.07521/) (2023)
- [Survey of Graph Neural Network for Internet of Things and NextG Networks](https://mlsanity.com/paper/2405.17309/) (2024)
- [Retinal Vessel Segmentation with Deep Graph and Capsule Reasoning](https://mlsanity.com/paper/2409.11508/) (2024)
- [Revisiting 2D Convolutional Neural Networks for Graph-based Applications](https://mlsanity.com/paper/2105.11016/) (2021)
- [Graph Learning-based Fleet Scheduling for Urban Air Mobility under Operational Constraints, Varying Demand & Uncertainties](https://mlsanity.com/paper/2401.04851/) (2024)
- [Bigraph Matching Weighted with Learnt Incentive Function for Multi-Robot Task Allocation](https://mlsanity.com/paper/2403.07131/) (2024)
- [A Graph-based Adversarial Imitation Learning Framework for Reliable & Realtime Fleet Scheduling in Urban Air Mobility](https://mlsanity.com/paper/2407.12113/) (2024)
- [Capsule Graph Neural Networks with EM Routing](https://mlsanity.com/paper/2110.09039/) (2021)
- [Distribution Preserving Graph Representation Learning](https://mlsanity.com/paper/2202.13428/) (2022)
- [Unified GCNs: Towards Connecting GCNs with CNNs](https://mlsanity.com/paper/2204.12300/) (2022)
- [Graph-level Neural Networks: Current Progress and Future Directions](https://mlsanity.com/paper/2205.15555/) (2022)
- [A Multi-scale Graph Signature for Persistence Diagrams based on Return Probabilities of Random Walks](https://mlsanity.com/paper/2209.14264/) (2022)
- [Large-Language-Model Empowered Dose Volume Histogram Prediction for Intensity Modulated Radiotherapy](https://mlsanity.com/paper/2402.07167/) (2024)
- [SPGNN: Recognizing Salient Subgraph Patterns via Enhanced Graph Convolution and Pooling](https://mlsanity.com/paper/2404.13655/) (2024)
- [Automated Generation of Diverse Courses of Actions for Multi-Agent Operations using Binary Optimization and Graph Learning](https://mlsanity.com/paper/2506.20031/) (2025)
- [Topology-Aware Reinforcement Learning over Graphs for Resilient Power Distribution Networks](https://mlsanity.com/paper/2603.06964/) (2026)
- [Designing Active Tether-Net Systems for Space Debris Capture with Graph-Learning-Aided Mixed-Combinatorial Optimization](https://mlsanity.com/paper/2605.29021/) (2026)

## 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.