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}
}