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Graph Neural Networks for Fault Diagnostics in Cyber-Physical Systems: A Survey of Taxonomy, Deployment Architectures and Failure Modes
1 School of Computing, Montclair State University, Montclair, NJ 07043, USA
2 AWS Corporation, USA
*whom correspondence should be addressed. E-mail: tiwariv1@montclair.edu, othmanr@montclair.edu
Received: 3 May 2026, Revised: 14 June 2026, Accepted: 16 June 2026, Published Online: 23 June 2026
(This article belongs to the Section Interdisciplinary Applications – Computer Science (IAC))
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APA Style
Tiwari, V. , Suaifan, O. , Othman, R. and Gupta, A. (2026). Graph Neural Networks for Fault Diagnostics in Cyber-Physical Systems: A Survey of Taxonomy, Deployment Architectures and Failure Modes. Journal of Engineering Research and Sciences, 5(6), 67–96. https://doi.org/10.55708/js0506006
Tiwari, V. , Suaifan, O. , Othman, R. and Gupta, A. (2026). Graph Neural Networks for Fault Diagnostics in Cyber-Physical Systems: A Survey of Taxonomy, Deployment Architectures and Failure Modes. Journal of Engineering Research and Sciences, 5(6), 67–96. https://doi.org/10.55708/js0506006
Chicago/Turabian Style
Vaibhavi Tiwari, Ola Suaifan, Ramy Othman and Anand Gupta. "Graph Neural Networks for Fault Diagnostics in Cyber-Physical Systems: A Survey of Taxonomy, Deployment Architectures and Failure Modes." Journal of Engineering Research and Sciences 5, no. 6 (June 2026): 67–96. https://doi.org/10.55708/js0506006
Vaibhavi Tiwari, Ola Suaifan, Ramy Othman and Anand Gupta. "Graph Neural Networks for Fault Diagnostics in Cyber-Physical Systems: A Survey of Taxonomy, Deployment Architectures and Failure Modes." Journal of Engineering Research and Sciences 5, no. 6 (June 2026): 67–96. https://doi.org/10.55708/js0506006
IEEE Style
V. Tiwari, O. Suaifan, R. Othman and A. Gupta, "Graph Neural Networks for Fault Diagnostics in Cyber-Physical Systems: A Survey of Taxonomy, Deployment Architectures and Failure Modes," Journal of Engineering Research and Sciences, vol. 5, no. 6, pp. 67–96, Jun. 2026, doi: 10.55708/js0506006.
V. Tiwari, O. Suaifan, R. Othman and A. Gupta, "Graph Neural Networks for Fault Diagnostics in Cyber-Physical Systems: A Survey of Taxonomy, Deployment Architectures and Failure Modes," Journal of Engineering Research and Sciences, vol. 5, no. 6, pp. 67–96, Jun. 2026, doi: 10.55708/js0506006.
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Abstract
Graph Neural Networks (GNNs) have emerged as a promising approach for fault diagnosis in complex cyber-physical systems because they can model intercomponent relationships, fault propagation, and system-level anomalies across domains such as industrial automation, smart grids, transportation, and healthcare. This survey presents a multidimensional review of GNN-based fault diagnostics, organizing existing methods according to graph representation, learning paradigm, diagnostic objective, and deployment context. It examines commonly used benchmark datasets, evaluation protocols, and cloud, edge, hybrid, and federated deployment architectures, with particular attention to reproducibility and practical implementation. In addition to methodological limitations, the survey identifies operational failure modes, including cascading misdiagnosis, topology drift, noise amplification, open-set misclassification, adversarial vulnerability, and concept drift, and examines their implications for safety-critical systems. Emerging research directions, including physics-informed learning, multimodal fusion, dynamic graph modeling, and privacy-preserving federated GNNs, are discussed alongside their ethical and safety implications. Unlike reviews centered primarily on diagnostic tasks or application domains, this survey integrates methodological, architectural, and operational perspectives within a unified framework. The analysis indicates that although GNNs offer capabilities for dependency-aware fault diagnosis, their practical deployment remains constrained by inconsistent benchmarking, sensitivity to graph construction, computational requirements, limited interpretability, and insufficient validation under evolving operational conditions. Finally, practitioneroriented design guidelines are presented to support the development of reliable, robust, and deployable GNN-based diagnostic systems and to connect algorithmic advances with the operational reliability requirements of next-generation fault-tolerant infrastructures.
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References
- Siemens AG, “The true cost of downtime 2024: How much do leading manufacturers lose through inefficient maintenance?”, Tech. rep., Siemens Digital Industries, 2024.
- ABB, “Industrial downtime costs up to $500,000 per hour and can happen every week”, https://new.abb.com/news/detail/130589/industrial-downtime-costs-up-to-500000-per-hour-and-can-happen-every-week, 2025, accessed: 2026-06-21.
- Q. Li, Z. Wu, “Knowledge graph-enhanced fault diagnosis: A bibliometric review of AI applications in sensor management (1998–2024)”, Discover Artificial Intelligence, vol. 5, p. 417, 2025, doi: 10.1007/s44163-025-00688-w.
- DataIntelo, “Graph neural networks market research report 2033”, https://dataintelo.com/report/graph-neural-networks-market, 2025, reports global GNN market size of USD 1.45 billion in 2024 and projected USD 15.47 billion by 2033 at 30.2% CAGR; accessed: 2026-06-21.
- Precedence Research, “Neural network market size, share, and trends 2025 to 2034”, https://www.precedenceresearch.com/neural-network-market, 2025, accessed: 2026-06-21.
- Y. Liu, B. Shen, D. S.-H. Wong, M. Jia, Y. Yao, “Explainable neural network meets graph neural network: Recent advances in process fault detection and diagnosis”, Computers & Chemical Engineering, vol. 206, p. 109528, 2026, doi: 10.1016/j.compchemeng.2025.109528.
- S. Yang, R. Liu, “A review of graph neural networks for rolling bearing fault diagnosis”, Measurement Science and Technology, 2026, doi: 10.1088/1361-6501/ae2e29.
- J. Liu, Y. Huang, K. Chen, G. Liu, J. Yan, S. Chen, Y. Xie, Y. Yu, T. Huang, “Graph neural networks for fault diagnosis in photovoltaic-integrated distribution networks with weak features”, Sensors, vol. 25, no. 18, p. 5691, 2025, doi: 10.3390/s25185691.
- W. Ouyang, Y. Jin, “Fault diagnosis of process systems based on graph neural network”, in Proceedings of the 1st International Conference on Data Mining, E-Learning, and Information Systems (DMEIS 2024), pp. 37–45, SCITEPRESS, 2024, doi: 10.5220/0012876300004536.
- M. Aurangzeb, Y. Wang, S. Iqbal, M. Shafiullah, S. A. Mohammed, Z. M. S. Elbarbary, A. Rehman, “Robust fault detection and uncertainty quantification in smart grids using graph neural networks”, Energy Reports, vol. 15, p. 108920, 2026, doi: 10.1016/j.egyr.2025.12.057.
- B. Karabulut, C. Manna, C. Develder, “Generalization of graph neural network models for distribution grid fault detection”, in 2025 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), pp. 1–7, IEEE, 2025, doi: 10.1109/SmartGridComm65349.2025.11204594.
- X. Wang, H. Wang, M. Peng, “Interpretability study of a typical fault diagnosis model for nuclear power plant primary circuit based on a graph neural network”, Reliability Engineering & System Safety, vol. 261, p. 111151, 2025, doi: 10.1016/j.ress.2025.111151.
- M. J. Page, J. E. McKenzie, P. M. Bossuyt, I. Boutron, T. C. Hoffmann, C. D. Mulrow, L. Shamseer, J. M. Tetzlaff, E. A. Akl, S. E. Brennan, R. Chou, J. Glanville, J. Grimshaw, A. Hróbjartsson, M. M. Lalu, T. Li, E. W. Loder, E. Mayo-Wilson, S. McDonald, L. A. McGuinness, L. A. Stewart, J. Thomas, A. C. Tricco, V. A. Welch, P. Whiting, D. Moher, “The PRISMA 2020 statement: An updated guideline for reporting systematic reviews”, BMJ, vol. 372, p. n71, 2021, doi: 10.1136/bmj.n71.
- Z. Chen, J. Xu, C. Alippi, S. X. Ding, Y. A. W. Shardt, T. Peng, C. Yang, “Graph neural network-based fault diagnosis: A review”, arXiv preprint arXiv:2111.08185, 2021, doi: 10.48550/arXiv.2111.08185.
- M. Jia, Y. Yao, Y. Liu, “Review on graph neural networks for process soft sensor development, fault diagnosis, and process monitoring”, Industrial & Engineering Chemistry Research, vol. 64, no. 17, pp. 8543–8564, 2025, doi: 10.1021/acs.iecr.5c00283.
- J. Zhu, J. Hu, B. Sheng, “Multi-metric fusion hypergraph neural network for rotating machinery fault diagnosis”, Actuators, vol. 14, no. 5, p. 242, 2025, doi: 10.3390/act14050242.
- R. Liu, Q. Zhang, D. Lin, W. Zhang, S. X. Ding, “Causal intervention graph neural network for fault diagnosis of complex industrial processes”, Reliability Engineering & System Safety, vol. 251, p. 110328, 2024, doi: 10.1016/j.ress.2024.110328.
- W. Xiao, Y. Wan, Z. Wang, C. Chen, “Spatio-temporal graph neural network for fault diagnosis modeling of industrial robot”, in Proceedings of the 31st International Conference on Engineering, Technology, and Innovation (ICE/ITMC), pp. 1–9, IEEE, 2025, doi: 10.1109/ICE/ITMC65658.2025.11106644.
- B. Wang, M. Wang, Y. Xu, L. Wang, S. Chen, X. Chen, “A diagnosis method based on graph neural networks embedded with multirelationships of intrinsic mode functions for multiple mechanical faults”, Defence Technology, vol. 50, pp. 364–373, 2025, doi: 10.1016/j.dt.2025.04.014.
- C. Li, L. Mo, C. K. Kwoh, X. Li, Z. Chen, M. Wu, R. Yan, “Noise-robust multi-view graph neural network for fault diagnosis of rotating machinery”, Mechanical Systems and Signal Processing, vol. 224, p. 112025, 2025, doi: 10.1016/j.ymssp.2024.112025.
- G. Jiang, K. Shen, X. Liu, X. Cheng, P. Xie, “Hierarchical spatio-temporal graph network for fault diagnosis of industrial processes”, IEEE Internet of Things Journal, vol. 12, no. 3, pp. 3043–3054, 2025, doi: 10.1109/JIOT.2024.3476287.
- Y. Wang, M. Wu, X. Li, L. Xie, Z. Chen, “A survey on graph neural networks for remaining useful life prediction: Methodologies, evaluation and future trends”, arXiv preprint arXiv:2409.19629, 2024, doi: 10.48550/arXiv.2409.19629.
- Z. Chen, J. Xu, T. Peng, C. Yang, “Graph convolutional network-based method for fault diagnosis using a hybrid of measurement and prior knowledge”, IEEE Transactions on Cybernetics, vol. 52, no. 9, pp. 9157–9169, 2022, doi: 10.1109/TCYB.2021.3059002.
- W. Liao, D. Yang, Y. Wang, X. Ren, “Fault diagnosis of power transformers using graph convolutional network”, CSEE Journal of Power and Energy Systems, vol. 7, no. 2, pp. 241–249, 2021, doi: 10.17775/CSEEJPES.2020.04120.
- Q.-H. Ngo, B. L. H. Nguyen, J. Zhang, K. Schoder, H. Ginn, T. Vu, “Deep graph neural network for fault detection and identification in distribution systems”, Electric Power Systems Research, vol. 247, p. 111721, 2025, doi: 10.1016/j.epsr.2025.111721.
- W. Li, D. Deka, “Physics-informed graph neural networks for robust fault location in power grids”, ICML 2021 Workshop on Tackling Climate Change with Machine Learning, 2021.
- T. Li, Z. Zhao, C. Sun, R. Yan, X. Chen, “Hierarchical attention graph convolutional network to fuse multi-sensor signals for remaining useful life prediction”, Reliability Engineering & System Safety, vol. 215, p. 107878, 2021, doi: 10.1016/j.ress.2021.107878.
- Q. Wang, B. Han, “Temporal transaction network anomaly detection for industrial internet of things with federated graph neural networks”, Computers & Industrial Engineering, vol. 205, p. 111122, 2025, doi: 10.1016/j.cie.2025.111122.
- Z. He, Y. Zeng, H. Shao, B. Yang, “Noise-robust rotating machinery fault diagnosis method based on enhanced graph isomorphism network with multi-sensor signals”, Measurement Science and Technology, 2025, doi: 10.1088/1361-6501/adf45d.
- Y. Wang, J. Zhao, D. Tang, W. Zhao, S. Huang, “Intelligent fault prediction and diagnosis for wind-powered heating systems using graph neural networks”, Scientific Reports, vol. 15, p. 39068, 2025, doi: 10.1038/s41598-025-25884-7.
- M. Vaida, Z. Huang, “Multimodal graph neural networks in healthcare: A review of fusion strategies across biomedical domains”, Frontiers in Artificial Intelligence, vol. 8, p. 1716706, 2026, doi: 10.3389/frai.2025.1716706.
- C. Lou, M. A. Atoui, X. Zhang, D. Xu, H. Zhong, “Semi-supervised graph neural networks for fault diagnosis in marine machinery”, Engineering Applications of Artificial Intelligence, vol. 177, no. 2, p. 114951, 2026, doi: 10.1016/j.engappai.2026.114951.
- R. Bourgerie, T. Zanouda, “Fault detection in telecom networks using bi-level federated graph neural networks”, in 2023 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 1608–1617, IEEE, 2023, doi: 10.1109/ICDMW60847.2023.10449399.
- V. P. Dwivedi, C. K. Joshi, A. T. Luu, T. Laurent, Y. Bengio, X. Bresson, “Benchmarking graph neural networks”, Journal of Machine Learning Research, vol. 24, no. 43, pp. 1–48, 2023.
- A. Varbella, K. Amara, B. Gjorgiev, M. El-Assady, G. Sansavini, “PowerGraph: A power grid benchmark dataset for graph neural networks”, Advances in Neural Information Processing Systems, vol. 37, pp. 110784–110804, 2024, doi: 10.52202/079017-3517.
- Case Western Reserve University Bearing Data Center, “Bearing Data Center”, https://engineering.case.edu/bearingdatacenter, accessed: 2026-06-13.
- C. Lessmeier, J. K. Kimotho, D. Zimmer, W. Sextro, “Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification”, Proceedings of the European Conference of the Prognostics and Health Management Society, vol. 3, 2016, doi: 10.36001/phme.2016.v3i1.1577.
- J. Lee, H. Qiu, G. Yu, J. Lin, R. T. Services, “IMS, University of Cincinnati Bearing Data Set”, NASA Prognostics Data Repository, NASA Ames Research Center, 2007.
- H. Qiu, J. Lee, J. Lin, G. Yu, “Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics”, Journal of Sound and Vibration, vol. 289, no. 4–5, pp. 1066–1090, 2006, doi: 10.1016/j.jsv.2005.03.007.
- Y. Lei, T. Han, B. Wang, N. Li, T. Yan, J. Yang, “XJTU-SY rolling element bearing accelerated life test datasets: A tutorial”, Journal of Mechanical Engineering, vol. 55, no. 16, pp. 1–6, 2019, doi: 10.3901/JME.2019.16.001.
- B. Wang, Y. Lei, N. Li, N. Li, “A hybrid prognostics approach for estimating remaining useful life of rolling element bearings”, IEEE Transactions on Reliability, vol. 69, no. 1, pp. 401–412, 2020, doi: 10.1109/TR.2018.2882682.
- H. Purohit, R. Tanabe, K. Ichige, T. Endo, Y. Nikaido, K. Suefusa, Y. Kawaguchi, “MIMII dataset: Sound dataset for malfunctioning industrial machine investigation and inspection”, Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop, pp. 209–213, 2019, doi: 10.33682/m76f-d618.
- R. Tanabe, H. Purohit, K. Dohi, T. Endo, Y. Nikaido, T. Nakamura, Y. Kawaguchi, “MIMII DUE: Sound dataset for malfunctioning industrial machine investigation and inspection with domain shifts due to changes in operational and environmental conditions”, in 2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), pp. 21–25, 2021, doi: 10.1109/WASPAA52581.2021.9632774, also available as arXiv:2105.02702.
- A. Saxena, K. Goebel, “Turbofan engine degradation simulation data set”, NASA Prognostics Data Repository, NASA Ames Research Center, 2008.
- A. Saxena, K. Goebel, D. Simon, N. Eklund, “Damage propagation modeling for aircraft engine run-to-failure simulation”, in 2008 International Conference on Prognostics and Health Management, pp. 1–9, 2008, doi: 10.1109/PHM.2008.4711414.
- J. J. Downs, E. F. Vogel, “A plant-wide industrial process control problem”, Computers & Chemical Engineering, vol. 17, no. 3, pp. 245–255, 1993, doi: 10.1016/0098-1354(93)80018-I.
- S. Ghamizi, A. Bojchevski, A. Ma, J. Cao, “SafePowerGraph: Safety-aware evaluation of graph neural networks for transmission power grids”, arXiv preprint arXiv:2407.12421, 2024, doi: 10.48550/arXiv.2407.12421.
- Z. Zhu, F. Li, Z. Mo, Q. Hu, G. Li, Z. Liu, X. Liang, J. Cheng, “A²Q: Aggregation-aware quantization for graph neural networks”, in International Conference on Learning Representations, 2023.
- A. Zhou, J. Yang, T. Qiao, Y. Qi, Z. Yang, W. Zhao, C. Hu, “Graph neural networks automated design and deployment on device-edge co-inference systems”, in Proceedings of the 61st ACM/IEEE Design Automation Conference, DAC ’24, Association for Computing Machinery, New York, NY, USA, 2024, doi: 10.1145/3649329.3655938.
- J. Qin, R. Yang, N. Yu, “Physics-informed graph neural networks for collaborative dynamic reconfiguration and voltage regulation in unbalanced distribution systems”, IEEE Transactions on Industry Applications, vol. 61, no. 2, pp. 2538–2548, 2025, doi: 10.1109/TIA.2025.3529799.
- A. Isah, H. Shin, I. Aliyu, R. M. Sulaiman, J. Kim, “Graph neural network for digital twin network: A conceptual framework”, in 2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 1–5, IEEE, 2024, doi: 10.1109/ICAIIC60209.2024.10463455.
- S. Sheka, A. Saraswathi, “Improving mechanical fault diagnosis using graph neural networks with dynamic and multiscale features”, Engineering, Technology & Applied Science Research, vol. 15, no. 4, pp. 25382–25387, 2025, doi: 10.48084/etasr.11612.
- Y. Wang, “FedGNN-SFD: A lightweight federated graph neural network for multi-sensor bearing fault diagnosis in industrial IoT systems”, Preprints.org, 2026, doi: 10.20944/preprints202603.2130.v1, preprint.
- B. Chen, G. Lu, Y. Zhu, C. Liu, B. Qin, J. Li, “GCFR: Graph contrastive fault representation for diagnosis in power communication networks”, Scientific Reports, vol. 15, p. 39838, 2025, doi: 10.1038/s41598-025-23457-2.
- F. Wang, Y. Liu, K. Liu, Y. Wang, S. Medya, P. S. Yu, “Uncertainty in graph neural networks: A survey”, Transactions on Machine Learning Research, 2025, doi: 10.48550/arXiv.2403.07185.
- Q. Zhou, L. Xue, J. He, S. Jia, Y. Li, “A rotating machinery fault diagnosis method based on dynamic graph convolution network and hard threshold denoising”, Sensors, vol. 24, no. 15, p. 4887, 2024, doi: 10.3390/s24154887.
- S. Zhang, S. Shan, Z. Hu, Y. Shen, C. Li, K. Zhang, H. Wei, “Out-of-distribution fault detection in multi-sensor systems using spatio-temporal dynamic graph neural networks”, Mechanical Systems and Signal Processing, vol. 241, p. 113524, 2025, doi: 10.1016/j.ymssp.2025.113524.
- J. Xu, Y. Wang, R. Xu, H. Wang, X. Zhou, “Research on open-set recognition methods for rolling bearing fault diagnosis”, Sensors, vol. 25, no. 10, p. 3019, 2025, doi: 10.3390/s25103019.
- Y. Jin, X. Zhu, “Oversmoothing alleviation in graph neural networks: A survey and unified view”, Knowledge and Information Systems, vol. 67, pp. 11259–11285, 2025, doi: 10.1007/s10115-025-02548-6.
- D. Kelesis, D. Fotakis, G. Paliouras, “Analyzing the effect of residual connections to oversmoothing in graph neural networks”, Machine Learning, vol. 114, p. 184, 2025, doi: 10.1007/s10994-025-06822-0.
- U. Alon, E. Yahav, “On the bottleneck of graph neural networks and its practical implications”, in International Conference on Learning Representations, 2021.
- A. Vassilev, A. Oprea, A. Fordyce, H. Anderson, X. Davies, M. Hamin, “Adversarial machine learning: A taxonomy and terminology of attacks and mitigations”, Tech. Rep. NIST AI 100-2e2025, National Institute of Standards and Technology, 2025, doi: 10.6028/NIST.AI.100-2e2025.
- L. Gosch, M. Sabanayagam, D. Ghoshdastidar, S. Günnemann, “Provable robustness of (graph) neural networks against data poisoning and backdoor attacks”, Transactions on Machine Learning Research, 2025.
- V. Toğan, F. Mostofi, O. B. Tokdemir, “Evaluating the resilience of graph neural network architectures to adversarial and noisy data in high-stakes construction project management”, Journal of Construction Engineering and Management, vol. 152, no. 4, p. 04026029, 2026, doi: 10.1061/JCEMD4.COENG-17244.
- Z. He, C. Shen, B. Chen, J. Shi, W. Huang, Z. Zhu, D. Wang, “A new feature boosting based continual learning method for bearing fault diagnosis with incremental fault types”, Advanced Engineering Informatics, vol. 61, p. 102469, 2024, doi: 10.1016/j.aei.2024.102469.
- G. Mao, H. Li, L. Xue, Y. Li, Z. Cai, K. Noman, “FedPM-SGN: A federated graph network for aviation equipment fault diagnosis by multi-sensor fusion in decentralized and heterogeneous setting”, Information Fusion, vol. 117, p. 102876, 2025, doi: 10.1016/j.inffus.2024.102876.
- Z. Ma, C. Liu, F. He, G. Yang, “A physics-informed graph convolutional network for bearing fault diagnosis via dynamic constraint-guided feature learning”, Engineering Research Express, vol. 8, p. 065524, 2026, doi: 10.1088/2631-8695/ae5166.
- S. Yu, X. Li, Y. Lei, B. Yang, N. Li, K. Feng, “Multimodal data-enabled large model for machine fault diagnosis towards intelligent operation and maintenance”, Journal of Industrial Information Integration, vol. 50, p. 101061, 2026, doi: 10.1016/j.jii.2026.101061.
- T. Li, Z. Zhou, S. Li, C. Sun, R. Yan, X. Chen, “The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study”, Mechanical Systems and Signal Processing, vol. 168, p. 108653, 2022, doi: 10.1016/j.ymssp.2021.108653.
- M. MansourLakouraj, M. Gautam, R. Hossain, H. Livani, M. Benidris, S. Commuri, “Event classification in active distribution grids using physics-informed graph neural network and PMU measurements”, in 2022 IEEE Industry Applications Society Annual Meeting (IAS), pp. 1–6, IEEE, 2022, doi: 10.1109/IAS54023.2022.9939922.
- C. He, K. Balasubramanian, E. Ceyani, C. Yang, H. Xie, L. Sun, L. He, L. Yang, P. S. Yu, Y. Rong, P. Zhao, J. Huang, M. Annavaram, S. Avestimehr, “FedGraphNN: A federated learning system and benchmark for graph neural networks”, 2021, doi: 10.48550/arXiv.2104.07145.
- D. Zügner, O. Borchert, A. Akbarnejad, S. Günnemann, “Adversarial attacks on graph neural networks: Perturbations and their patterns”, ACM Transactions on Knowledge Discovery from Data, vol. 14, no. 5, pp. 57:1–57:31, 2020, doi: 10.1145/3394520.
- D. Paolini, P. Dini, A. Elhanashi, S. Saponara, “Advanced fault detection and diagnosis exploiting machine learning and artificial intelligence for engineering applications”, Electronics, vol. 15, no. 2, p. 476, 2026, doi: 10.3390/electronics15020476.
- H. Zhang, B. Wu, X. Yuan, S. Pan, H. Tong, J. Pei, “Trustworthy graph neural networks: Aspects, methods, and trends”, Proceedings of the IEEE, vol. 112, no. 2, pp. 97–139, 2024, doi: 10.1109/JPROC.2024.3369017.
- E. Tabassi, “Artificial intelligence risk management framework (AI RMF 1.0)”, Tech. Rep. NIST AI 100-1, National Institute of Standards and Technology, 2023, doi: 10.6028/NIST.AI.100-1.
- H. Yuan, H. Yu, S. Gui, S. Ji, “Explainability in graph neural networks: A taxonomic survey”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 5, pp. 5782–5799, 2023, doi: 10.1109/TPAMI.2022.3204236.
- C. Wu, F. Wu, L. Lyu, T. Qi, Y. Huang, X. Xie, “A federated graph neural network framework for privacy-preserving personalization”, Nature Communications, vol. 13, no. 1, p. 3091, 2022, doi: 10.1038/s41467-022-30714-9.
- A. Chen, R. A. Rossi, N. Park, P. Trivedi, Y. Wang, T. Yu, S. Kim, F. Dernoncourt, N. K. Ahmed, “Fairness-aware graph neural networks: A survey”, 2023, doi: 10.48550/arXiv.2307.03929.
- W. Jin, Y. Li, H. Xu, Y. Wang, S. Ji, C. Aggarwal, J. Tang, “Adversarial attacks and defenses on graphs: A review, a tool and empirical studies”, 2020, doi: 10.48550/arXiv.2003.00653.
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