# Pooling in Graph Convolutional Neural Networks

@article{Cheung2019PoolingIG, title={Pooling in Graph Convolutional Neural Networks}, author={Mark Cheung and John Shi and Lavender Yao Jiang and Oren Wright and Jos{\'e} M. F. Moura}, journal={2019 53rd Asilomar Conference on Signals, Systems, and Computers}, year={2019}, pages={462-466} }

Graph convolutional neural networks (GCNNs) are a powerful extension of deep learning techniques to graph-structured data problems. We empirically evaluate several pooling methods for GCNNs, and combinations of those graph pooling methods with three different architectures: GCN, TAGCN, and GraphSAGE. We confirm that graph pooling, especially DiffPool, improves classification accuracy on popular graph classification datasets and find that, on average, TAGCN achieves comparable or better accuracy…

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## References

SHOWING 1-10 OF 25 REFERENCES

### Hierarchical Graph Representation Learning with Differentiable Pooling

- Computer ScienceNeurIPS
- 2018

DiffPool is proposed, a differentiable graph pooling module that can generate hierarchical representations of graphs and can be combined with various graph neural network architectures in an end-to-end fashion.

### Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

- Computer ScienceNIPS
- 2016

This work presents a formulation of CNNs in the context of spectral graph theory, which provides the necessary mathematical background and efficient numerical schemes to design fast localized convolutional filters on graphs.

### Clique pooling for graph classification

- Computer ScienceArXiv
- 2019

A novel graph pooling operation using cliques as the unit pool is proposed, more readily interpretable, a better analogue to image coarsening than filtering or pruning techniques, and entirely nonparametric.

### ON GRAPH CONVOLUTION FOR GRAPH CNNS

- Computer Science2018 IEEE Data Science Workshop (DSW)
- 2018

This paper analyzes these two types of graph convolutional layers and demonstrates that the spectrum domain based graph convo-lutional layer suffers from output inconsistencies when the graph shift matrix has repeated eigenvalues.

### Classification with Vertex-Based Graph Convolutional Neural Networks

- Computer Science2018 52nd Asilomar Conference on Signals, Systems, and Computers
- 2018

TAGCN is used to classify different time periods during the week based on New York City taxi data defined on a directed graph with an accuracy of 88% using a single graph convolutional layer.

### Semi-Supervised Classification with Graph Convolutional Networks

- Computer ScienceICLR
- 2017

A scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs which outperforms related methods by a significant margin.

### Spectral Networks and Locally Connected Networks on Graphs

- Computer ScienceICLR
- 2014

This paper considers possible generalizations of CNNs to signals defined on more general domains without the action of a translation group, and proposes two constructions, one based upon a hierarchical clustering of the domain, and another based on the spectrum of the graph Laplacian.

### Self-Attention Graph Pooling

- Computer ScienceICML
- 2019

This paper proposes a graph pooling method based on self-attention using graph convolution, which achieves superior graph classification performance on the benchmark datasets using a reasonable number of parameters.

### Topology adaptive graph convolutional networks

- Computer ScienceArXiv
- 2017

The proposed TAGCN not only inherits the properties of convolutions in CNN for grid-structured data, but it is also consistent with convolution as defined in graph signal processing, exhibiting better performance than existing graph-convolution-approximation methods on a number of data sets.

### An End-to-End Deep Learning Architecture for Graph Classification

- Computer ScienceAAAI
- 2018

This paper designs a localized graph convolution model and shows its connection with two graph kernels, and designs a novel SortPooling layer which sorts graph vertices in a consistent order so that traditional neural networks can be trained on the graphs.