# Iso-CapsNet: Isomorphic Capsule Network for Brain Graph Representation Learning

arXiv: https://arxiv.org/abs/2206.13465 · HTML: https://mlsanity.com/paper/2206.13465/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 43 references that are in the map.
- First posted on arXiv 27 Jun 2022.

## Builds on (most cited first, 22 of 22)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [Geometric deep learning on graphs and manifolds using mixture model CNNs](https://mlsanity.com/paper/1611.08402/) (2016)
- [Learning Convolutional Neural Networks for Graphs](https://mlsanity.com/paper/1605.05273/) (2016)
- [Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search](https://mlsanity.com/paper/1810.10659/) (2018)
- [Zero-shot User Intent Detection via Capsule Neural Networks](https://mlsanity.com/paper/1809.00385/) (2018)
- [DeepCaps: Going Deeper with Capsule Networks](https://mlsanity.com/paper/1904.09546/) (2019)
- [GraphNAS: Graph Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1904.09981/) (2019)
- [Capsules for Object Segmentation](https://mlsanity.com/paper/1804.04241/) (2018)
- [GResNet: Graph Residual Network for Reviving Deep GNNs from Suspended Animation](https://mlsanity.com/paper/1909.05729/) (2019)
- [Dual Directed Capsule Network for Very Low Resolution Image Recognition](https://mlsanity.com/paper/1908.10027/) (2019)
- [Capsule Networks for Brain Tumor Classification based on MRI Images and Course Tumor Boundaries](https://mlsanity.com/paper/1811.00597/) (2018)
- [Graph analysis of functional brain networks: practical issues in translational neuroscience](https://mlsanity.com/paper/1406.7391/) (2014)
- [Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications](https://mlsanity.com/paper/1906.02829/) (2019)
- [Geodesic convolutional neural networks on Riemannian manifolds](https://mlsanity.com/paper/1501.06297/) (2015)
- [Multi-labeled Relation Extraction with Attentive Capsule Network](https://mlsanity.com/paper/1811.04354/) (2018)
- [IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification](https://mlsanity.com/paper/1907.09495/) (2019)
- [Deep Loopy Neural Network Model for Graph Structured Data Representation Learning](https://mlsanity.com/paper/1805.07504/) (2018)

## Abstract (arXiv)

Brain graph representation learning serves as the fundamental technique for brain diseases diagnosis. Great efforts from both the academic and industrial communities have been devoted to brain graph representation learning in recent years. The isomorphic neural network (IsoNN) introduced recently can automatically learn the existence of sub-graph patterns in brain graphs, which is also the state-of-the-art brain graph representation learning method by this context so far. However, IsoNN fails to capture the orientations of sub-graph patterns, which may render the learned representations to be useless for many cases. In this paper, we propose a new Iso-CapsNet (Isomorphic Capsule Net) model by introducing the graph isomorphic capsules for effective brain graph representation learning. Based on the capsule dynamic routing, besides the subgraph pattern existence confidence scores, Iso-CapsNet can also learn other sub-graph rich properties, including position, size and orientation, for calculating the class-wise digit capsules. We have compared Iso-CapsNet with both classic and state-of-the-art brain graph representation approaches with extensive experiments on four brain graph benchmark datasets. The experimental results also demonstrate the effectiveness of Iso-CapsNet, which can out-perform the baseline methods with significant improvements.