Journal
CONFERENCE RECORD OF THE 2019 FIFTY-THIRD ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS
Volume -, Issue -, Pages 462-466Publisher
IEEE
DOI: 10.1109/ieeeconf44664.2019.9048796
Keywords
graph convolutional neural network; graph pooling; TAGCN; graph classification; graph signal processing
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Funding
- Department of Defense [FA8702-15-D-0002]
- Carnegie Mellon University
- NSF [CCF 1837607, CCN 1513936]
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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.
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