Feb. 15, 2024, 5:41 a.m. | Tianxiang Zhao, Xiang Zhang, Suhang Wang

cs.LG updates on arXiv.org arxiv.org

arXiv:2402.08824v1 Announce Type: new
Abstract: Graph Neural Networks (GNNs) have demonstrated significant success in learning from graph-structured data across various domains. Despite their great successful, one critical challenge is often overlooked by existing works, i.e., the learning of message propagation that can generalize effectively to underrepresented graph regions. These minority regions often exhibit irregular homophily/heterophily patterns and diverse neighborhood class distributions, resulting in ambiguity. In this work, we investigate the ambiguity problem within GNNs, its impact on representation learning, and …

abstract arxiv challenge classification cs.lg cs.si data domains gnns graph graph neural networks networks neural networks node propagation structured data success type

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