NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning

Wentao Zhang · Zeang Sheng · Mingyu Yang · Yang Li · Yu Shen · Zhi Yang · Bin Cui

Hall E #636

Keywords: [ MISC: Unsupervised and Semi-supervised Learning ] [ MISC: Scalable Algorithms ] [ DL: Graph Neural Networks ] [ MISC: Representation Learning ]

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Wed 20 Jul 3:30 p.m. PDT — 5:30 p.m. PDT
Spotlight presentation: MISC: Representation Learning/Causality
Wed 20 Jul 10:15 a.m. PDT — 11:45 a.m. PDT


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 present node-adaptive feature smoothing (NAFS), a simple non-parametric method that constructs node representations without parameter learning. NAFS first extracts the features of each node with its neighbors of different hops by feature smoothing, and then adaptively combines the smoothed features. Besides, the constructed node representation can further be enhanced by the ensemble of smoothed features extracted via different smoothing strategies. We conduct experiments on four benchmark datasets on two different application scenarios: node clustering and link prediction. Remarkably, NAFS with feature ensemble outperforms the state-of-the-art GNNs on these tasks and mitigates the aforementioned two limitations of most learning-based GNN counterparts.

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