Summary of Robust Graph Structure Learning Under Heterophily, by Xuanting Xie et al.
Robust Graph Structure Learning under Heterophily
by Xuanting Xie, Zhao Kang, Wenyu Chen
First submitted to arxiv on: 6 Mar 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 The proposed method tackles the problem of learning robust graph structures from noisy and sparse data, which is crucial for various downstream tasks. The current state-of-the-art methods in graph representation learning assume a homophilic graph, where most connected nodes are from the same class. However, this assumption does not hold when dealing with heterophilic graphs, where most connected nodes are from different classes. To address this challenge, the authors introduce a novel method that first applies a high-pass filter to make each node more distinctive by incorporating structure information into node features. Then, an adaptive norm is learned to characterize different levels of noise in the graph. A regularizer is also proposed to refine the graph structure further. The effectiveness of the method is demonstrated through clustering and semi-supervised classification experiments on heterophilic graphs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to create accurate and complete graphs from noisy and incomplete data. Most current methods assume that the graph is correct, but real-world data can be messy and missing information. This new approach first makes each node in the graph more unique by adding details about its connections. Then, it learns how to handle different levels of noise in the graph. Finally, it refines the graph structure to make it more accurate. The method was tested on graphs with nodes from different classes and showed promising results. |
Keywords
* Artificial intelligence * Classification * Clustering * Representation learning * Semi supervised
