Summary of Fully-inductive Node Classification on Arbitrary Graphs, by Jianan Zhao et al.
Fully-inductive Node Classification on Arbitrary Graphs
by Jianan Zhao, Zhaocheng Zhu, Mikhail Galkin, Hesham Mostafa, Michael Bronstein, Jian Tang
First submitted to arxiv on: 30 May 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 GraphAny model presents a fully-inductive setup for graph machine learning, allowing models to perform inference on arbitrary test graphs with new structures, feature, and label spaces. By fusing multiple LinearGNN predictions with learned inductive attention scores, GraphAny demonstrates superior generalization capabilities compared to both inductive and transductive baselines. Specifically, a single model trained on the Wisconsin dataset achieves an average accuracy of 67.26% when applied to 30 new graphs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GraphAny is a new way for machine learning models to learn from graph data. Usually, these models can only work with new data that has the same characteristics as the training data. But GraphAny allows models to work on entirely new types of graphs, which is very useful because real-world data often comes in many different forms. The model uses a combination of learned attention scores and LinearGNN predictions to make accurate predictions even when it encounters completely new data. |
Keywords
» Artificial intelligence » Attention » Generalization » Inference » Machine learning
