Summary of Tsc: a Simple Two-sided Constraint Against Over-smoothing, by Furong Peng et al.
TSC: A Simple Two-Sided Constraint against Over-Smoothing
by Furong Peng, Kang Liu, Xuan Lu, Yuhua Qian, Hongren Yan, Chao Ma
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 presents a novel approach to address the limitations of Graph Convolutional Neural Networks (GCNs) when analyzing relational data. GCNs typically rely on aggregating neighboring information to enhance node discriminability, but stacking multiple layers can lead to the over-smoothing problem. The authors identify two key causes of this issue: (a) neighbor quality, where high-order neighbors become indistinguishable due to overlapping features, and (b) neighbor quantity, where exponentially growing aggregated neighbors overshadow the initial feature. Current solutions often focus on one or the other, but this paper proposes a unified approach that considers both factors simultaneously. The authors demonstrate the effectiveness of their method using various benchmarks and applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to figure out what makes people similar or different based on who they know. Graph Convolutional Neural Networks (GCNs) are a type of computer program that helps with this task. However, these programs can get stuck when looking at too much information at once. This paper proposes a new way to fix this problem by considering two main issues: the quality and quantity of the people or groups being compared. The authors show how their approach can be used in various real-world scenarios and improves the accuracy of GCNs. |
