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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2022.3204236
|2 doi
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|a pubmed25n1152.xml
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|a eng
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| 100 |
1 |
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|a Yuan, Hao
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Explainability in Graph Neural Networks
|b A Taxonomic Survey
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| 264 |
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1 |
|c 2023
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 11.04.2023
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| 500 |
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|a Date Revised 06.01.2025
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| 500 |
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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| 520 |
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|a 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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| 650 |
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|a Journal Article
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| 650 |
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4 |
|a Research Support, U.S. Gov't, Non-P.H.S.
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| 700 |
1 |
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|a Yu, Haiyang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Gui, Shurui
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ji, Shuiwang
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 5 vom: 19. Mai, Seite 5782-5799
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnas
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| 773 |
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|g volume:45
|g year:2023
|g number:5
|g day:19
|g month:05
|g pages:5782-5799
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|u http://dx.doi.org/10.1109/TPAMI.2022.3204236
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