How Powerful are Graph Neural Networks

ICLR 2018
Published: Oct 1, 2018
Abstract
Graph Neural Networks (GNNs) are an effective framework for representation learning of graphs. GNNs follow a neighborhood aggregation scheme, where the representation vector of a node is computed by recursively aggregating and transforming representation vectors of its neighboring nodes. Many GNN variants have been proposed and have achieved state-of-the-art results on both node and graph classification tasks. However, despite GNNs...
Paper Details
Title
How Powerful are Graph Neural Networks
Published Date
Oct 1, 2018
Journal
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