GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking Article Swipe
Mert Kosan
,
Samidha Verma
,
Burouj Armgaan
,
Khushbu Pahwa
,
Ambuj K. Singh
,
Sourav Medya
,
Sayan Ranu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.01794
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.01794
Numerous explainability methods have been proposed to shed light on the inner workings of GNNs. Despite the inclusion of empirical evaluations in all the proposed algorithms, the interrogative aspects of these evaluations lack diversity. As a result, various facets of explainability pertaining to GNNs, such as a comparative analysis of counterfactual reasoners, their stability to variational factors such as different GNN architectures, noise, stochasticity in non-convex loss surfaces, feasibility amidst domain constraints, and so forth, have yet to be formally investigated. Motivated by this need, we present a benchmarking study on perturbation-based explainability methods for GNNs, aiming to systematically evaluate and compare a wide range of explainability techniques. Among the key findings of our study, we identify the Pareto-optimal methods that exhibit superior efficacy and stability in the presence of noise. Nonetheless, our study reveals that all algorithms are affected by stability issues when faced with noisy data. Furthermore, we have established that the current generation of counterfactual explainers often fails to provide feasible recourses due to violations of topological constraints encoded by domain-specific considerations. Overall, this benchmarking study empowers stakeholders in the field of GNNs with a comprehensive understanding of the state-of-the-art explainability methods, potential research problems for further enhancement, and the implications of their application in real-world scenarios.
Related Topics
Concepts
Benchmarking
Counterfactual thinking
Computer science
Stability (learning theory)
Machine learning
Artificial intelligence
Perturbation (astronomy)
Benchmark (surveying)
Mathematical optimization
Mathematics
Psychology
Economics
Social psychology
Management
Geography
Geodesy
Quantum mechanics
Physics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2310.01794
- https://arxiv.org/pdf/2310.01794
- OA Status
- green
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387355946
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4387355946Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2310.01794Digital Object Identifier
- Title
-
GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth BenchmarkingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-03Full publication date if available
- Authors
-
Mert Kosan, Samidha Verma, Burouj Armgaan, Khushbu Pahwa, Ambuj K. Singh, Sourav Medya, Sayan RanuList of authors in order
- Landing page
-
https://arxiv.org/abs/2310.01794Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2310.01794Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2310.01794Direct OA link when available
- Concepts
-
Benchmarking, Counterfactual thinking, Computer science, Stability (learning theory), Machine learning, Artificial intelligence, Perturbation (astronomy), Benchmark (surveying), Mathematical optimization, Mathematics, Psychology, Economics, Social psychology, Management, Geography, Geodesy, Quantum mechanics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 3Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.facets | 38 |
| abstract_inverted_index.forth, | 74 |
| abstract_inverted_index.issues | 142 |
| abstract_inverted_index.noise, | 62 |
| abstract_inverted_index.noise. | 130 |
| abstract_inverted_index.study, | 114 |
| abstract_inverted_index.Despite | 15 |
| abstract_inverted_index.aspects | 28 |
| abstract_inverted_index.compare | 101 |
| abstract_inverted_index.current | 154 |
| abstract_inverted_index.encoded | 171 |
| abstract_inverted_index.exhibit | 121 |
| abstract_inverted_index.factors | 56 |
| abstract_inverted_index.further | 199 |
| abstract_inverted_index.methods | 2, 93, 119 |
| abstract_inverted_index.present | 86 |
| abstract_inverted_index.provide | 162 |
| abstract_inverted_index.result, | 36 |
| abstract_inverted_index.reveals | 134 |
| abstract_inverted_index.various | 37 |
| abstract_inverted_index.Numerous | 0 |
| abstract_inverted_index.Overall, | 175 |
| abstract_inverted_index.affected | 139 |
| abstract_inverted_index.analysis | 48 |
| abstract_inverted_index.efficacy | 123 |
| abstract_inverted_index.empowers | 179 |
| abstract_inverted_index.evaluate | 99 |
| abstract_inverted_index.feasible | 163 |
| abstract_inverted_index.findings | 111 |
| abstract_inverted_index.formally | 79 |
| abstract_inverted_index.identify | 116 |
| abstract_inverted_index.methods, | 194 |
| abstract_inverted_index.presence | 128 |
| abstract_inverted_index.problems | 197 |
| abstract_inverted_index.proposed | 5, 24 |
| abstract_inverted_index.research | 196 |
| abstract_inverted_index.superior | 122 |
| abstract_inverted_index.workings | 12 |
| abstract_inverted_index.Motivated | 81 |
| abstract_inverted_index.different | 59 |
| abstract_inverted_index.empirical | 19 |
| abstract_inverted_index.inclusion | 17 |
| abstract_inverted_index.potential | 195 |
| abstract_inverted_index.recourses | 164 |
| abstract_inverted_index.stability | 53, 125, 141 |
| abstract_inverted_index.surfaces, | 67 |
| abstract_inverted_index.algorithms | 137 |
| abstract_inverted_index.diversity. | 33 |
| abstract_inverted_index.explainers | 158 |
| abstract_inverted_index.generation | 155 |
| abstract_inverted_index.non-convex | 65 |
| abstract_inverted_index.pertaining | 41 |
| abstract_inverted_index.real-world | 208 |
| abstract_inverted_index.reasoners, | 51 |
| abstract_inverted_index.scenarios. | 209 |
| abstract_inverted_index.violations | 167 |
| abstract_inverted_index.algorithms, | 25 |
| abstract_inverted_index.application | 206 |
| abstract_inverted_index.comparative | 47 |
| abstract_inverted_index.constraints | 170 |
| abstract_inverted_index.established | 151 |
| abstract_inverted_index.evaluations | 20, 31 |
| abstract_inverted_index.feasibility | 68 |
| abstract_inverted_index.techniques. | 107 |
| abstract_inverted_index.topological | 169 |
| abstract_inverted_index.variational | 55 |
| abstract_inverted_index.Furthermore, | 148 |
| abstract_inverted_index.Nonetheless, | 131 |
| abstract_inverted_index.benchmarking | 88, 177 |
| abstract_inverted_index.constraints, | 71 |
| abstract_inverted_index.enhancement, | 200 |
| abstract_inverted_index.implications | 203 |
| abstract_inverted_index.stakeholders | 180 |
| abstract_inverted_index.comprehensive | 188 |
| abstract_inverted_index.interrogative | 27 |
| abstract_inverted_index.investigated. | 80 |
| abstract_inverted_index.stochasticity | 63 |
| abstract_inverted_index.understanding | 189 |
| abstract_inverted_index.Pareto-optimal | 118 |
| abstract_inverted_index.architectures, | 61 |
| abstract_inverted_index.counterfactual | 50, 157 |
| abstract_inverted_index.explainability | 1, 40, 92, 106, 193 |
| abstract_inverted_index.systematically | 98 |
| abstract_inverted_index.considerations. | 174 |
| abstract_inverted_index.domain-specific | 173 |
| abstract_inverted_index.state-of-the-art | 192 |
| abstract_inverted_index.perturbation-based | 91 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |