The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Explainability in Graph Neural Networks: A Taxonomic Survey
Open accessAbstract
Deep learning methods are achieving ever-increasing performance on many artificial intelligence tasks. A major limitation of deep models is that they are not amenable to interpretability. This limitation can be circumvented by developing post hoc techniques to explain predictions, giving rise to the area of explainability. Recently, explainability of deep models on images and texts has achieved significant progress. In the area of graph data, graph neural networks (GNNs) and their explainability are experiencing rapid developments. However, there is neither a unified treatment of GNN explainability methods, nor a standard benchmark and testbed for evaluations. In this survey, we provide a unified and taxonomic view of current GNN explainability methods. Our unified and taxonomic treatments of this subject shed lights on the commonalities and differences of existing methods and set the stage for further methodological developments. To facilitate evaluations, we provide a testbed for GNN explainability, including datasets, common algorithms and evaluation metrics. Furthermore, we conduct comprehensive experiments to compare and analyze the performance of many techniques. Altogether, this work provides a unified methodological treatment of GNN explainability and a standardized testbed for evaluations.
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Liu, H., Wang, Y., Fan, W., Liu, X., Li, Y., Jain, S., Liu, Y., Jain, A., & Tang, J. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3546872 · Cited by 261
Evaluating explainability for graph neural networks
Agarwal, C., Queen, O., Lakkaraju, H., & Zitnik, M. (2023) · Scientific Data · doi:10.1038/s41597-023-01974-x · Cited by 186
A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research
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Metadata
- Authors
- Yuan, H., Yu, H., Gui, S., & Ji, S.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2022
- Volume
- 45
- Issue
- 5
- Pages
- 1-19
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W3116637551
- Cited by
- 583
- References
- 16
- FWCI
- 40.47
- Type
- journal article
- Language
- en
- Open access
- green
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- Yuan, H., Yu, H., Gui, S., & Ji, S. (2022). Explainability in Graph Neural Networks: A Taxonomic Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2022.3204236
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