June 6, 2024, 4:42 a.m. | Michael Scholkemper, Xinyi Wu, Ali Jadbabaie, Michael Schaub

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.02997v1 Announce Type: new
Abstract: Residual connections and normalization layers have become standard design choices for graph neural networks (GNNs), and were proposed as solutions to the mitigate the oversmoothing problem in GNNs. However, how exactly these methods help alleviate the oversmoothing problem from a theoretical perspective is not well understood. In this work, we provide a formal and precise characterization of (linearized) GNNs with residual connections and normalization layers. We establish that (a) for residual connections, the incorporation of …

abstract arxiv become cs.lg design gnns graph graph neural networks however networks neural networks normalization perspective problem residual solutions standard type

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