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Aggregation Graph Neural Networks

Citation Author(s):
Fernando Gama, Antonio G. Marques, Geert Leus, Alejandro Ribeiro
Submitted by:
Fernando Gama
Last updated:
15 May 2019 - 2:34pm
Document Type:
Presentation Slides
Document Year:
Fernando Gama
Paper Code:


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.

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