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Self-supervised role learning for graph neural networks

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Abstract

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 formulate attributed structural roles of nodes based on their occurrence in different network motifs, independent of network proximity. Network motifs are higher-order structures indicating connectivity patterns between nodes and are crucial to the organization of complex networks. Two nodes share attributed structural roles if they participate in topologically similar motif instances over covarying sets of attributes. InfoMotif achieves architecture-agnostic regularization of arbitrary GNNs through novel self-supervised learning objectives based on mutual information maximization. Our training curriculum dynamically prioritizes multiple motifs in the learning process without relying on distributional assumptions in the underlying graph or the learning task. We integrate three state-of-the-art GNNs in our framework, to show notable performance gains (3–10% accuracy) across nine diverse real-world datasets spanning homogeneous and heterogeneous networks. Notably, we see stronger gains for nodes with sparse training labels and diverse attributes in local neighborhood structures.

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Notes

  1. The terms network motif, graphlet, and induced subgraph are used interchangeably in graph mining literature.

  2. https://github.com/CrowdDynamicsLab/InfoMotif.

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Acknowledgements

We thank anonymous reviewers for their very useful comments and suggestions. Part of this work was done, while Li Shen and Ling Cheng were doing research in Griffith University. The work was supported by Australian Research Council (ARC) Large Grant A849602031.

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  1. Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA

    Aravind Sankar, Junting Wang, Adit Krishnan & Hari Sundaram

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Sankar, A., Wang, J., Krishnan, A. et al. Self-supervised role learning for graph neural networks. Knowl Inf Syst 64, 2091–2121 (2022). https://doi.org/10.1007/s10115-022-01694-5

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