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Aggregation Graph Neural Networks
![](https://sigport.org/sites/default/files/styles/home/public/ICASSP_2019_0.jpg?itok=OnlvXQeZ)
- Citation Author(s):
- Submitted by:
- Fernando Gama
- Last updated:
- 15 May 2019 - 2:34pm
- Document Type:
- Presentation Slides
- Document Year:
- 2019
- Event:
- Presenters:
- Fernando Gama
- Paper Code:
- 2457
- Categories:
- Keywords:
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Graph neural networks (GNNs) regularize classical neural networks by exploiting the underlying irregular structure supporting graph data, extending its application to broader data domains. The aggregation GNN presented here is a novel GNN that exploits the fact that the data collected at a single node by means of successive local exchanges with neighbors exhibits a regular structure. Thus, regular convolution and regular pooling yield an appropriately regularized GNN. To address some scalability issues that arise when collecting all the information at a single node, we propose a multi-node aggregation GNN that constructs regional features that are later aggregated into more global features and so on. We show superior performance in a source localization problem on synthetic graphs and on the authorship attribution problem.