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NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning. (arXiv:2206.08583v1 [cs.LG])
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 graph representation learning lg representation representation learning
More from arxiv.org / cs.LG updates on arXiv.org
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