Web: http://arxiv.org/abs/2206.08583

June 20, 2022, 1:10 a.m. | Wentao Zhang, Zeang Sheng, Mingyu Yang, Yang Li, Yu Shen, Zhi Yang, Bin Cui

cs.LG updates on arXiv.org arxiv.org

Recently, graph neural networks (GNNs) have shown prominent performance in
graph representation learning by leveraging knowledge from both graph structure
and node features. However, most of them have two major limitations. First,
GNNs can learn higher-order structural information by stacking more layers but
can not deal with large depth due to the over-smoothing issue. Second, it is
not easy to apply these methods on large graphs due to the expensive
computation cost and high memory usage. In this paper, we …

arxiv graph learning lg representation representation learning

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