Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Beyond Homophily in Graph Neural Networks: Current Limitations and
  Effective Designs   We investigate the representation power of graph neural networks in the
semi-supervised node classification task under heterophily or low homophily,
i.e., in networks where connected nodes may have different class labels and
dissimilar features. Many popular GNNs fail to generalize to this setting, and
are even outperformed by models that ignore the graph structure (e.g.,
multilayer perceptrons). Motivated by this limitation, we identify a set of key
designs -- ego- and neighbor-embedding separation, higher-order neighborhoods,
and combination of intermediate representations -- that boost learning from the
graph structure under heterophily. We combine them into a graph neural network,
H2GCN, which we use as the base method to empirically evaluate the
effectiveness of the identified designs. Going beyond the traditional
benchmarks with strong homophily, our empirical analysis shows that the
identified designs increase the accuracy of GNNs by up to 40% and 27% over
models without them on synthetic and real networks with heterophily,
respectively, and yield competitive performance under homophily.

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The categories of this paper are: eess
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The categories of this paper are: dg-ga
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The categories of this paper are: solv-int
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The categories of this paper are: adap-org
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The categories of this paper are: nlin
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The categories of this paper are: quant-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: alg-geom
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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-th
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The categories of this paper are: comp-gas
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The categories of this paper are: chem-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: funct-an
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The categories of this paper are: ao-sci