Summary of Is Graph Convolution Always Beneficial For Every Feature?, by Yilun Zheng et al.
Is Graph Convolution Always Beneficial For Every Feature?
by Yilun Zheng, Xiang Li, Sitao Luan, Xiaojiang Peng, Lihui Chen
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper proposes a novel metric, Topological Feature Informativeness (TFI), to assess the impact of graph convolution on feature dimensions in Graph Neural Networks (GNNs). Traditional GNNs treat each feature dimension equally, but this paper shows that not all features benefit equally from the convolution operation. The authors introduce a simple yet effective method, Graph Feature Selection (GFS), which processes GNN-favored and GNN-disfavored features separately using GNNs and non-GNN models. Compared to original GNNs, GFS improves the extraction of useful topological information with comparable computational costs. Extensive experiments on 8 baseline and state-of-the-art GNN architectures across 10 datasets show that 83.75% of GFS-augmented cases exhibit significant performance boosts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research paper is about making Graph Neural Networks (GNNs) work better by choosing the right features to use. GNNs are really good at processing data that has a special structure, but some features might not be as helpful as others. The authors created a new way to measure how well different features work with the GNN, called Topological Feature Informativeness (TFI). They also developed a simple method to select the best features for each GNN model. When they tested this method on many different models and datasets, it worked really well, improving performance by a significant amount. |
Keywords
» Artificial intelligence » Feature selection » Gnn
