Summary of Towards Effective Graph Rationalization Via Boosting Environment Diversity, by Yujie Wang et al.
Towards Effective Graph Rationalization via Boosting Environment Diversity
by Yujie Wang, Kui Yu, Yuhong Zhang, Fuyuan Cao, Jiye Liang
First submitted to arxiv on: 17 Dec 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 Graph Rationalization by Boosting Environmental diversity (GRBE) approach aims to improve the generalizability of Graph Neural Networks (GNNs) when training and testing graphs are drawn from different distributions. The method generates augmented samples in the original graph space, ensuring both effectiveness and diversity. A precise rationale subgraph extraction strategy is introduced to refine the rationale subgraph learning process, while an environment diversity augmentation strategy combines environment subgraphs of different graphs with rationale subgraphs to generate diverse augmented graphs. Experimental results on benchmark datasets show average improvements of 7.65% and 6.11% in rationalization and classification performance, outperforming state-of-the-art approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to help Graph Neural Networks (GNNs) work well even when the training and testing data are different. The method is called Graph Rationalization by Boosting Environmental diversity, or GRBE for short. It makes the GNN better by generating new, diverse samples of graphs that can help it learn and generalize more effectively. To do this, GRBE uses a special way to extract the most important parts of the graph, called rationale subgraphs, and then mixes them with other parts of the graph to create new, diverse samples. The results show that GRBE is better than current methods at solving certain problems. |
Keywords
» Artificial intelligence » Boosting » Classification » Gnn
