Globally Interpretable Graph Learning via Distribution Matching
Author:
Affiliation:
1. University of Chicago, Chicago, USA
2. Pennsylvania State University, State College, PA, USA
3. Emory University, Atlanta, GA, USA
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3589334.3645674
Reference40 articles.
1. Evaluating explainability for graph neural networks
2. Steve Azzolin, Antonio Longa, Pietro Barbiero, Pietro Liò , and Andrea Passerini. 2022. Global explainability of gnns via logic combination of learned concepts. arXiv preprint arXiv:2210.07147 (2022).
3. Federico Baldassarre and Hossein Azizpour. 2019. Explainability techniques for graph convolutional networks. arXiv preprint arXiv:1905.13686 (2019).
4. Dataset Distillation by Matching Training Trajectories
5. Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity
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