Pooling in Graph Convolutional Neural Networks
arXiv:2004.03519
Abstract
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 than GCN and GraphSAGE, particularly for datasets with larger and sparser graph structures.
5 pages, 2 figures, 2019 Asilomar Conference paper
References in corpus (8)
- Semi-Supervised Classification with Graph Convolutional Networks
- Geometric deep learning: going beyond Euclidean data
- Discrete Signal Processing on Graphs
- Fast Graph Representation Learning with PyTorch Geometric
- Discrete Signal Processing on Graphs: Sampling Theory
- Efficient Sampling Set Selection for Bandlimited Graph Signals Using Graph Spectral Proxies
- Topology Adaptive Graph Convolutional Networks
- Clique pooling for graph classification