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arXiv · 1806.00770

Dual-Primal Graph Convolutional Networks

Federico Monti·Oleksandr Shchur·Aleksandar Bojchevski·Or Litany·Stephan Günnemann·Michael M. Bronstein
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Abstract

In recent years, there has been a surge of interest in developing deep learning methods for non-Euclidean structured data such as graphs. In this paper, we propose Dual-Primal Graph CNN, a graph convolutional architecture that alternates convolution-like operations on the graph and its dual. Our approach allows to learn both vertex- and edge features and generalizes the previous graph attention (GAT) model. We provide extensive experimental validation showing state-of-the-art results on a variety of tasks tested on established graph benchmarks, including CORA and Citeseer citation networks as well as MovieLens, Flixter, Douban and Yahoo Music graph-guided recommender systems.

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Federico Monti, Oleksandr Shchur, Aleksandar Bojchevski, Or Litany, Stephan Günnemann, Michael M. Bronstein. 2018-06-03. Dual-Primal Graph Convolutional Networks. https://arxiv.org/abs/1806.00770

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