Beyond Localized Graph Neural Networks: An Attributed Motif Regularization Framework

@article{Sankar2020BeyondLG,
  title={Beyond Localized Graph Neural Networks: An Attributed Motif Regularization Framework},
  author={Aravind Sankar and Junting Wang and Adit Krishnan and H. Sundaram},
  journal={2020 IEEE International Conference on Data Mining (ICDM)},
  year={2020},
  pages={472-481}
}
We present InfoMotif, a new semi-supervised, motif-regularized, learning framework over graphs. We overcome two key limitations of message passing in popular graph neural networks (GNNs): localization (a $k$-layer GNN cannot utilize features outside the $k$-hop neighborhood of the labeled training nodes) and over-smoothed (structurally indistinguishable) representations. We propose the concept of attributed structural roles of nodes based on their occurrence in different network motifs… 

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