Web: http://arxiv.org/abs/2209.06589

Sept. 15, 2022, 1:11 a.m. | Hyungeun Lee, Hyunmok Park, Kijung Yoon

cs.LG updates on arXiv.org arxiv.org

Graph neural networks (GNNs) have become compelling models designed to
perform learning and inference on graph-structured data, but little work has
been done on understanding the fundamental limitations of GNNs to be scalable
to larger graphs and generalized to out-of-distribution inputs. In this paper,
we use a random graph generator that allows us to systematically investigate
how the graph size and structural properties affect the predictive performance
of GNNs. We present specific evidence that, among the many graph properties,
the …

arxiv graph graph neural networks networks neural networks representation

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