Summary of Graph Mining Under Data Scarcity, by Appan Rakaraddi et al.
Graph Mining under Data scarcity
by Appan Rakaraddi, Lam Siew-Kei, Mahardhika Pratama, Marcus de Carvalho
First submitted to arxiv on: 7 Jun 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 The proposed Uncertainty Estimator framework is designed to improve node classification performance in graph learning frameworks like Graph Neural Networks (GNNs) under labeled-data scarcity. The framework can be applied on top of any generic GNN backbone network, which are typically designed for supervised/semi-supervised node classification. By modeling the uncertainty estimator as a probability distribution rather than probabilistic discrete scalar values, the model demonstrates improved accuracy in few-shot settings without requiring meta-learning specific architecture. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Our work proposes an Uncertainty Estimator framework that can be used to improve node classification performance on graphs with GNNs. This framework is designed to work with any generic GNN backbone network and doesn’t require a lot of labeled data. We tested our method on multiple datasets and showed it works better than other methods. |
Keywords
» Artificial intelligence » Classification » Few shot » Gnn » Meta learning » Probability » Semi supervised » Supervised
