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Federico Monti, Oleksandr Shchur, Aleksandar Bojchevski, Or Litany, Stephan Gunnemann, Michael M Bronstein. GEM workshop at ECML-PKDD, 2019
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.
Bibtex:
@article{msblgb-dpgcn-19,
author = {Federico Monti and
Oleksandr Shchur and
Aleksandar Bojchevski and
Or Litany and
Stephan G{"{u}}nnemann and
Michael M. Bronstein},
title = {Dual-Primal Graph Convolutional Networks},
journal = {CoRR},
volume = {abs/1806.00770},
year = {2018}
}
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