Summary of Graph Neural Networks Are More Than Filters: Revisiting and Benchmarking From a Spectral Perspective, by Yushun Dong et al.
Graph Neural Networks Are More Than Filters: Revisiting and Benchmarking from A Spectral Perspective
by Yushun Dong, Patrick Soga, Yinhan He, Song Wang, Jundong Li
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper explores the performance of Graph Neural Networks (GNNs) in capturing and processing information encoded in different frequency components of input graph data. The study challenges the notion that neighborhood aggregation mechanisms dominate GNN behavior in the spectral domain, suggesting that other components like non-linear layers also play a significant role. To better understand this phenomenon, the authors introduce a comprehensive benchmark to measure and evaluate GNNs’ performance from a spectral perspective. They demonstrate that GNNs can produce outputs with diverse frequency components even when certain frequencies are absent or filtered out. The paper then formulates a novel research problem and designs an evaluation protocol supported by theoretical analysis. Finally, the authors provide a comprehensive benchmark on real-world datasets, revealing insights that challenge prevalent opinions from a spectral perspective. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how Graph Neural Networks (GNNs) work with graph data. GNNs are good at learning patterns in graphs, but some researchers think it’s not just because of one thing they do well. Instead, other parts of the network also matter. To figure this out, the authors created a test to see if GNNs can learn from different parts of the graph. They found that GNNs can make outputs with different patterns even when some patterns are missing or hidden. This study helps us understand how GNNs work and could lead to new ways to improve them. |
Keywords
» Artificial intelligence » Gnn
