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Computer Science > Machine Learning

arXiv:2309.04332 (cs)
[Submitted on 8 Sep 2023]

Title:Graph Neural Networks Use Graphs When They Shouldn't

Authors:Maya Bechler-Speicher, Ido Amos, Ran Gilad-Bachrach, Amir
Globerson
Download a PDF of the paper titled Graph Neural Networks Use Graphs
When They Shouldn't, by Maya Bechler-Speicher and 3 other authors
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    Abstract:Predictions over graphs play a crucial role in various
    domains, including social networks, molecular biology, medicine,
    and more. Graph Neural Networks (GNNs) have emerged as the
    dominant approach for learning on graph data. Instances of graph
    labeling problems consist of the graph-structure (i.e., the
    adjacency matrix), along with node-specific feature vectors. In
    some cases, this graph-structure is non-informative for the
    predictive task. For instance, molecular properties such as molar
    mass depend solely on the constituent atoms (node features), and
    not on the molecular structure. While GNNs have the ability to
    ignore the graph-structure in such cases, it is not clear that
    they will. In this work, we show that GNNs actually tend to
    overfit the graph-structure in the sense that they use it even
    when a better solution can be obtained by ignoring it. We examine
    this phenomenon with respect to different graph distributions and
    find that regular graphs are more robust to this overfitting. We
    then provide a theoretical explanation for this phenomenon, via
    analyzing the implicit bias of gradient-descent-based learning of
    GNNs in this setting. Finally, based on our empirical and
    theoretical findings, we propose a graph-editing method to
    mitigate the tendency of GNNs to overfit graph-structures that
    should be ignored. We show that this method indeed improves the
    accuracy of GNNs across multiple benchmarks.

Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as:  arXiv:2309.04332 [cs.LG]
          (or arXiv:2309.04332v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2309.04332
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Maya Bechler-Speicher [view email]
[v1] Fri, 8 Sep 2023 13:59:18 UTC (2,749 KB)
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