Summary of Game-theoretic Counterfactual Explanation For Graph Neural Networks, by Chirag Chhablani et al.
Game-theoretic Counterfactual Explanation for Graph Neural Networks
by Chirag Chhablani, Sarthak Jain, Akshay Channesh, Ian A. Kash, Sourav Medya
First submitted to arxiv on: 8 Feb 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 semivalue-based approach generates counterfactual explanations for node classification tasks in complex networks, eliminating the need for additional training. By computing Banzhaf values, this method achieves up to a fourfold speedup compared to Shapley values and is more efficient in noisy environments. The approach is demonstrated on three popular graph datasets, showing improved efficiency without compromising explanation quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier to understand why Graph Neural Networks (GNNs) make certain predictions by providing counterfactual explanations. Unlike previous approaches that require extra training, this new method uses a special kind of calculation called Banzhaf values. This approach is faster and more efficient than others, even when there’s noise in the data. The researchers tested it on three different types of graph datasets and found it works well. |
Keywords
* Artificial intelligence * Classification
