Summary of Learning Personalized Scoping For Graph Neural Networks Under Heterophily, by Gangda Deng et al.
Learning Personalized Scoping for Graph Neural Networks under Heterophily
by Gangda Deng, Hongkuan Zhou, Rajgopal Kannan, Viktor Prasanna
First submitted to arxiv on: 11 Sep 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 The proposed paper addresses a challenge in graph neural networks (GNNs) when dealing with heterophilous graphs, where nodes with different characteristics tend to connect. Traditional GNNs excel at aggregating homophilous information but struggle with heterophily. To overcome this limitation, the authors introduce personalized scoping, allowing each node to have a varying scope size. They formalize this concept as a separate scope classification problem that addresses GNN overfitting in node classification. The proposed Adaptive Scope (AS) approach encodes structural patterns and predicts the optimal depth for each node’s prediction. Experimental results demonstrate AS’s flexibility with various GNN architectures across multiple datasets, leading to improved accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to fix a problem with machine learning models that work well when similar things are connected, but struggle when different things connect. The authors want to make these models work better on real-world networks where different nodes often connect. They do this by letting each node have its own way of looking at the network and choosing which model to use for prediction. This approach helps the model avoid overfitting and makes it more accurate. |
Keywords
» Artificial intelligence » Classification » Gnn » Machine learning » Overfitting
