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Graph neural networks (GNNs) are popular machine learning models for graphs with many applications across scientic domains. However, GNNs are considered black box models, and it is challenging to understand how the model makes predictions. Game theoric Shapley value approaches are popular explanation methods in other domains but are not well-studied for graphs. Some studies have proposed Shapley value based GNN explanations, yet they have several limitations: they consider limited samples to approximate Shapley values; some mainly focus on small and large coalition sizes, and they are an order of magnitude slower than other explanation methods, making them inapplicable to even moderate-size graphs. In this work, we propose GNNShap, which provides explanations for edges since they provide more natural explanations for graphs and more ne-grained explanations. We overcome the limitations by sampling from all coalition sizes, parallelizing the sampling on GPUs, and speeding up model predictions by batching. GNNShap gives better delity scores and faster explanations than baselines on real-world datasets. The code is available at https://github.com/HipGraph/GNNShap.
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Efficient Sampling Approaches to Shapley Value Approximation
Shapley value provides a unique way to fairly assess each player's contribution in a coalition and has enjoyed many applications. However, the exact computation of Shapley value is #P-hard due to the combinatoric nature of Shapley value. Many existing applications of Shapley value are based on Monte-Carlo approximation, which requires a large number of samples and the assessment of utility on many coalitions to reach high quality approximation, and thus is still far from being efficient. Can we achieve an efficient approximation of Shapley value by smartly obtaining samples? In this paper, we treat the sampling approach to Shapley value approximation as a stratified sampling problem. Our main technical contributions are a novel stratification design and two sample allocation methods based on Neyman allocation and empirical Bernstein bound, respectively. Experimental results on several real data sets and synthetic data sets demonstrate the effectiveness and efficiency of our novel stratification design and sampling approaches.
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- PAR ID:
- 10448773
- Date Published:
- Journal Name:
- Proceedings of the ACM on Management of Data
- Volume:
- 1
- Issue:
- 1
- ISSN:
- 2836-6573
- Page Range / eLocation ID:
- 1 to 24
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3588728
