{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,31]],"date-time":"2026-05-31T11:00:32Z","timestamp":1780225232784,"version":"3.54.0"},"reference-count":76,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFB4606200"],"award-info":[{"award-number":["2023YFB4606200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100020774","name":"Major Projects of Special Development Funds in Zhangjiang National Independent Innovation Demonstration Zone, Shanghai","doi-asserted-by":"publisher","award":["ZJ2021-ZD-006"],"award-info":[{"award-number":["ZJ2021-ZD-006"]}],"id":[{"id":"10.13039\/501100020774","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.knosys.2026.116040","type":"journal-article","created":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T14:53:02Z","timestamp":1776523982000},"page":"116040","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Privacy-preserving Federated Graph Neural Network with Global Semantic Augmentation and Layer-Wise Node Alignment for Social Analysis"],"prefix":"10.1016","volume":"344","author":[{"given":"Yan","family":"Feng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3020-005X","authenticated-orcid":false,"given":"Quan","family":"Qian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2026.116040_b1","series-title":"Fedgnn: Federated graph neural network for privacy-preserving recommendation","author":"Wu","year":"2021"},{"key":"10.1016\/j.knosys.2026.116040_b2","doi-asserted-by":"crossref","unstructured":"Z. Liu, M. Wan, S. Guo, K. Achan, P.S. Yu, Basconv: Aggregating heterogeneous interactions for basket recommendation with graph convolutional neural network, in: Proceedings of the 2020 SIAM International Conference on Data Mining, 2020, pp. 64\u201372.","DOI":"10.1137\/1.9781611976236.8"},{"key":"10.1016\/j.knosys.2026.116040_b3","doi-asserted-by":"crossref","unstructured":"C. Yang, A. Pal, A. Zhai, N. Pancha, J. Han, C. Rosenberg, J. Leskovec, MultiSage: Empowering GCN with contextualized multi-embeddings on web-scale multipartite networks, in: Yang, Carl and Pal, Aditya and Zhai, Andrew and Pancha, Nikil and Han, Jiawei and Rosenberg, Charles and Leskovec, Jure, 2020, pp. 2434\u20132443.","DOI":"10.1145\/3394486.3403293"},{"key":"10.1016\/j.knosys.2026.116040_b4","article-title":"Enhancing intelligent marketing systems: a multi-layer hypernetwork approach integrating evidence theory for influential node identification","author":"Guo","year":"2025","journal-title":"Kybernetes"},{"issue":"4","key":"10.1016\/j.knosys.2026.116040_b5","doi-asserted-by":"crossref","first-page":"4682","DOI":"10.1109\/TNNLS.2021.3137396","article-title":"A comprehensive survey on community detection with deep learning","volume":"35","author":"Su","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"1","key":"10.1016\/j.knosys.2026.116040_b6","article-title":"Time-series nested reinforcement learning for dynamic risk control in nonlinear financial markets","volume":"5","author":"Yao","year":"2025","journal-title":"Trans. Comput. Sci. Methods"},{"issue":"12","key":"10.1016\/j.knosys.2026.116040_b7","doi-asserted-by":"crossref","first-page":"2257","DOI":"10.1109\/TKDE.2018.2819980","article-title":"Attributed social network embedding","volume":"30","author":"Liao","year":"2018","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.knosys.2026.116040_b8","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1007\/s10618-010-0210-x","article-title":"Leveraging social media networks for classification","volume":"23","author":"Tang","year":"2011","journal-title":"Data Min. Knowl. Discov."},{"issue":"5","key":"10.1016\/j.knosys.2026.116040_b9","doi-asserted-by":"crossref","first-page":"3409","DOI":"10.1007\/s11280-023-01192-w","article-title":"Huri: Hybrid user risk identification in social networks","volume":"26","author":"Corizzo","year":"2023","journal-title":"World Wide Web"},{"key":"10.1016\/j.knosys.2026.116040_b10","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1016\/j.inffus.2022.11.029","article-title":"SAIRUS: Spatially-aware identification of risky users in social networks","volume":"92","author":"Pellicani","year":"2023","journal-title":"Inform. Fusion"},{"key":"10.1016\/j.knosys.2026.116040_b11","series-title":"Federated learning of deep networks using model averaging","author":"McMahan","year":"2016"},{"issue":"3","key":"10.1016\/j.knosys.2026.116040_b12","doi-asserted-by":"crossref","first-page":"2031","DOI":"10.1109\/COMST.2020.2986024","article-title":"Federated learning in mobile edge networks: A comprehensive survey","volume":"22","author":"Lim","year":"2020","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"10.1016\/j.knosys.2026.116040_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2020.106854","article-title":"A review of applications in federated learning","volume":"149","author":"Li","year":"2020","journal-title":"Comput. Ind. Eng."},{"issue":"1\u20132","key":"10.1016\/j.knosys.2026.116040_b14","first-page":"1","article-title":"Advances and open problems in federated learning","volume":"14","author":"Kairouz","year":"2021","journal-title":"Found. Trends\u00ae Mach. Learn."},{"key":"10.1016\/j.knosys.2026.116040_b15","doi-asserted-by":"crossref","unstructured":"W. Huang, M. Ye, B. Du, Learn from others and be yourself in heterogeneous federated learning, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 10143\u201310153.","DOI":"10.1109\/CVPR52688.2022.00990"},{"key":"10.1016\/j.knosys.2026.116040_b16","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"Li","year":"2021","journal-title":"Proc. Mach. Learn. Syst."},{"key":"10.1016\/j.knosys.2026.116040_b17","unstructured":"S.P. Karimireddy, S. Kale, M. Mohri, S. Reddi, S. Stich, A.T. Suresh, Scaffold: Stochastic controlled averaging for federated learning, in: International Conference on Machine Learning, 2020, pp. 5132\u20135143."},{"key":"10.1016\/j.knosys.2026.116040_b18","doi-asserted-by":"crossref","unstructured":"Q. Li, B. He, D. Song, Model-contrastive federated learning, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 10713\u201310722.","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"10.1016\/j.knosys.2026.116040_b19","doi-asserted-by":"crossref","unstructured":"L. Gao, H. Fu, L. Li, Y. Chen, M. Xu, C.-Z. Xu, Feddc: Federated learning with non-iid data via local drift decoupling and correction, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 10112\u201310121.","DOI":"10.1109\/CVPR52688.2022.00987"},{"key":"10.1016\/j.knosys.2026.116040_b20","doi-asserted-by":"crossref","unstructured":"Y. Liu, Y. Zheng, D. Zhang, H. Chen, H. Peng, S. Pan, Towards unsupervised deep graph structure learning, in: Proceedings of the ACM Web Conference 2022, 2022, pp. 1392\u20131403.","DOI":"10.1145\/3485447.3512186"},{"key":"10.1016\/j.knosys.2026.116040_b21","doi-asserted-by":"crossref","unstructured":"W. Huang, G. Wan, M. Ye, B. Du, Federated graph semantic and structural learning, in: Proc. Int. Joint Conf. Artif. Intell, 2023, pp. 3830\u20133838.","DOI":"10.24963\/ijcai.2023\/426"},{"key":"10.1016\/j.knosys.2026.116040_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2023.119976","article-title":"Fedgl: Federated graph learning framework with global self-supervision","volume":"657","author":"Chen","year":"2024","journal-title":"Inform. Sci."},{"issue":"2","key":"10.1016\/j.knosys.2026.116040_b23","first-page":"6671","article-title":"Subgraph federated learning with missing neighbor generation","volume":"34","author":"Zhang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116040_b24","doi-asserted-by":"crossref","DOI":"10.1109\/TCSS.2025.3555566","article-title":"PPFedGNN: An efficient privacy-preserving federated graph neural network method for social network analysis","author":"Feng","year":"2025","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"10.1016\/j.knosys.2026.116040_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.129045","article-title":"Enhancing federated learning-based social recommendations with graph attention networks","volume":"617","author":"Xu","year":"2025","journal-title":"Neurocomputing"},{"issue":"3","key":"10.1016\/j.knosys.2026.116040_b26","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1007\/s10922-025-09928-x","article-title":"A novel framework for integrating blockchain-driven federated learning with neural networks in E-commerce","volume":"33","author":"Alshareet","year":"2025","journal-title":"J. Netw. Syst. Manage."},{"key":"10.1016\/j.knosys.2026.116040_b27","first-page":"1","article-title":"Self-Sustaining drone operations through deep reinforcement learning and piezoelectric energy harvesting","author":"Bahi","year":"2025","journal-title":"Int. J. Intell. Robot. Appl."},{"key":"10.1016\/j.knosys.2026.116040_b28","first-page":"1","article-title":"MycGNN: Enhancing recommendation diversity in E-commerce through mycelium-inspired graph neural network","author":"Bahi","year":"2024","journal-title":"Electron. Commer. Res."},{"issue":"13","key":"10.1016\/j.knosys.2026.116040_b29","doi-asserted-by":"crossref","first-page":"10229","DOI":"10.1007\/s00521-021-06135-y","article-title":"Citation recommendation employing heterogeneous bibliographic network embedding","volume":"34","author":"Ali","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.knosys.2026.116040_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.106438","article-title":"Paper recommendation based on heterogeneous network embedding","volume":"210","author":"Ali","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116040_b31","doi-asserted-by":"crossref","first-page":"1617","DOI":"10.1016\/j.ins.2022.06.075","article-title":"Influence maximization in social networks using graph embedding and graph neural network","volume":"607","author":"Kumar","year":"2022","journal-title":"Inform. Sci."},{"issue":"6","key":"10.1016\/j.knosys.2026.116040_b32","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.3390\/math10061000","article-title":"Fedgcn: Federated learning-based graph convolutional networks for non-euclidean spatial data","volume":"10","author":"Hu","year":"2022","journal-title":"Mathematics"},{"key":"10.1016\/j.knosys.2026.116040_b33","first-page":"18839","article-title":"Federated graph classification over non-iid graphs","volume":"34","author":"Xie","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116040_b34","article-title":"FedStar: Efficient federated learning on heterogeneous communication networks","author":"Cao","year":"2023","journal-title":"IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst."},{"key":"10.1016\/j.knosys.2026.116040_b35","series-title":"Vertically federated graph neural network for privacy-preserving node classification","author":"Chen","year":"2020"},{"issue":"12","key":"10.1016\/j.knosys.2026.116040_b36","doi-asserted-by":"crossref","first-page":"8464","DOI":"10.1109\/TII.2021.3055283","article-title":"FASTGNN: A topological information protected federated learning approach for traffic speed forecasting","volume":"17","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.knosys.2026.116040_b37","doi-asserted-by":"crossref","unstructured":"Q. Cao, H. Shen, J. Gao, B. Wei, X. Cheng, Popularity prediction on social platforms with coupled graph neural networks, in: Proceedings of the 13th International Conference on Web Search and Data Mining, 2020, pp. 70\u201378.","DOI":"10.1145\/3336191.3371834"},{"key":"10.1016\/j.knosys.2026.116040_b38","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2023.119976","article-title":"Fedgl: Federated graph learning framework with global self-supervision","volume":"657","author":"Chen","year":"2024","journal-title":"Inform. Sci."},{"issue":"2","key":"10.1016\/j.knosys.2026.116040_b39","first-page":"6671","article-title":"Subgraph federated learning with missing neighbor generation","volume":"34","author":"Zhang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116040_b40","doi-asserted-by":"crossref","unstructured":"P. Mohassel, Y. Zhang, SecureML: A System for Scalable Privacy-Preserving Machine Learning, in: 2017 IEEE Symposium on Security and Privacy, SP, Innsbruck, Austria, 2017, pp. 19\u201338.","DOI":"10.1109\/SP.2017.12"},{"key":"10.1016\/j.knosys.2026.116040_b41","doi-asserted-by":"crossref","unstructured":"L. Lu, N. Ding, Horizontal privacy-preserving linear regression which is highly efficient for dataset of low dimension, in: Proceedings of the 2021 ACM Asia Conference on Computer and Communications Security, Virtual Event Hong Kong, 2021, pp. 604\u2013615.","DOI":"10.1145\/3433210.3453105"},{"key":"10.1016\/j.knosys.2026.116040_b42","series-title":"Neural Information Processing Systems","first-page":"4961","article-title":"CrypTen: Secure multi-party computation meets machine learning","author":"Knott","year":"2021"},{"issue":"3","key":"10.1016\/j.knosys.2026.116040_b43","doi-asserted-by":"crossref","first-page":"26","DOI":"10.2478\/popets-2019-0035","article-title":"SecureNN:3-party secure computation for neural network training","volume":"2019","author":"Wagh","year":"2019","journal-title":"Proc. Priv. Enhancing Technol"},{"key":"10.1016\/j.knosys.2026.116040_b44","series-title":"A Fully Homomorphic Encryption Scheme","author":"Gentry","year":"2009"},{"key":"10.1016\/j.knosys.2026.116040_b45","doi-asserted-by":"crossref","unstructured":"I. Damg\u00e5rd, M. Jurik, A generalisation, a simplification and some applications of Paillier\u2019s probabilistic public-key system, in: The 4th International Workshop on Practice and Theory in Public Key Cryptosystems, 2001, pp. 119\u2013136.","DOI":"10.1007\/3-540-44586-2_9"},{"key":"10.1016\/j.knosys.2026.116040_b46","doi-asserted-by":"crossref","unstructured":"R. Cramer, I. Damg\u00e5rd, J.B. Nielsen, Multiparty computation from threshold homomorphic encryption, in: Proceedings of the International Conference on the Theory and Application of Cryptographic Techniques: Advances in Cryptology, Berlin, Heidelberg, 2001, pp. 280\u2013299.","DOI":"10.1007\/3-540-44987-6_18"},{"key":"10.1016\/j.knosys.2026.116040_b47","doi-asserted-by":"crossref","unstructured":"I. Damg\u00e5rd, J.B. Nielsen, Universally composable efficient multiparty computation from threshold homomorphic encryption, in: International Conference on the Theory and Applications of Cryptographic Techniques, Santa Barbara, California, USA, 2003, pp. 247\u2013264.","DOI":"10.1007\/978-3-540-45146-4_15"},{"issue":"3\u20134","key":"10.1016\/j.knosys.2026.116040_b48","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1561\/0400000042","article-title":"The algorithmic foundations of differential privacy","volume":"9","author":"Dwork","year":"2014","journal-title":"Foundations Trends\u00ae Theor. Comput. Sci."},{"key":"10.1016\/j.knosys.2026.116040_b49","unstructured":"D. Yu, H. Zhang, W. Chen, T.-Y. Liu, Do not let privacy overbill utility: Gradient embedding perturbation for private learning, in: International Conference on Learning Representations, 2021."},{"key":"10.1016\/j.knosys.2026.116040_b50","unstructured":"Y. Zhou, S. Wu, A. Banerjee, Bypassing the Ambient Dimension: Private {SGD} with Gradient Subspace Identification, in: International Conference on Learning Representations, 2021."},{"key":"10.1016\/j.knosys.2026.116040_b51","doi-asserted-by":"crossref","unstructured":"N. Phan, X. Wu, H. Hu, D. Dou, Adaptive laplace mechanism: Differential privacy preservation in deep learning, in: 2017 IEEE International Conference on Data Mining, ICDM, New Orleans, LA, USA, 2017, pp. 385\u2013394.","DOI":"10.1109\/ICDM.2017.48"},{"key":"10.1016\/j.knosys.2026.116040_b52","doi-asserted-by":"crossref","unstructured":"J. Ren, L. Jiang, H. Peng, L. Lyu, Z. Liu, C. Chen, J. Wu, X. Bai, P.S. Yu, Cross-network social user embedding with hybrid differential privacy guarantees, in: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta GA USA, 2022, pp. 1685\u20131695.","DOI":"10.1145\/3511808.3557278"},{"issue":"4","key":"10.1016\/j.knosys.2026.116040_b53","first-page":"1","article-title":"Federated social recommendation with graph neural network","volume":"13","author":"Liu","year":"2022","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"10.1016\/j.knosys.2026.116040_b54","doi-asserted-by":"crossref","unstructured":"C. Chen, J. Zhou, L. Zheng, H. Wu, L. Lyu, J. Wu, B. Wu, Z. Liu, L. Wang, X. Zheng, Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification, in: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, 2021.","DOI":"10.24963\/ijcai.2022\/272"},{"issue":"2","key":"10.1016\/j.knosys.2026.116040_b55","doi-asserted-by":"crossref","first-page":"3321","DOI":"10.32604\/cmc.2025.067044","article-title":"Interpretable vulnerability detection in LLMs: A BERT-based approach with SHAP explanations","volume":"85","author":"Ahmad","year":"2025","journal-title":"Comput. Mater. Continua"},{"issue":"1","key":"10.1016\/j.knosys.2026.116040_b56","article-title":"Mitigating adversarial obfuscation in named entity recognition with robust SecureBERT finetuning","volume":"87","author":"Ahmad","year":"2026","journal-title":"Comput. Mater. Continua"},{"key":"10.1016\/j.knosys.2026.116040_b57","doi-asserted-by":"crossref","unstructured":"Y. Liu, S. Ma, Y. Aafer, W.-C. Lee, J. Zhai, W. Wang, X. Zhang, Trojaning attack on neural networks, in: 25th Annual Network and Distributed System Security Symposium, NDSS 2018, San Diego, California, 2018.","DOI":"10.14722\/ndss.2018.23291"},{"key":"10.1016\/j.knosys.2026.116040_b58","series-title":"Feature squeezing: Detecting adversarial examples in deep neural networks","author":"Xu","year":"2017"},{"key":"10.1016\/j.knosys.2026.116040_b59","doi-asserted-by":"crossref","unstructured":"B. Hitaj, G. Ateniese, F. Perez-Cruz, Deep models under the GAN: information leakage from collaborative deep learning, in: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, Dallas Texas USA, 2017, pp. 603\u2013618.","DOI":"10.1145\/3133956.3134012"},{"key":"10.1016\/j.knosys.2026.116040_b60","doi-asserted-by":"crossref","unstructured":"R. Shokri, M. Stronati, C. Song, V. Shmatikov, Membership Inference Attacks Against Machine Learning Models, in: 2017 IEEE Symposium on Security and Privacy, SP, San Jose, CA, 2017, pp. 3\u201318.","DOI":"10.1109\/SP.2017.41"},{"key":"10.1016\/j.knosys.2026.116040_b61","doi-asserted-by":"crossref","unstructured":"Y. Liu, Y. Zheng, D. Zhang, H. Chen, H. Peng, S. Pan, Towards unsupervised deep graph structure learning, in: Proceedings of the ACM Web Conference 2022, 2022, pp. 1392\u20131403.","DOI":"10.1145\/3485447.3512186"},{"key":"10.1016\/j.knosys.2026.116040_b62","unstructured":"C. Zheng, B. Zong, W. Cheng, D. Song, J. Ni, W. Yu, H. Chen, W. Wang, Robust graph representation learning via neural sparsification, in: International Conference on Machine Learning, 2020, pp. 11458\u201311468."},{"key":"10.1016\/j.knosys.2026.116040_b63","doi-asserted-by":"crossref","unstructured":"M. Liu, H. Gao, S. Ji, Towards deeper graph neural networks, in: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020, pp. 338\u2013348.","DOI":"10.1145\/3394486.3403076"},{"key":"10.1016\/j.knosys.2026.116040_b64","doi-asserted-by":"crossref","unstructured":"K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H.B. McMahan, S. Patel, D. Ramage, A. Segal, K. Seth, Practical secure aggregation for privacy-preserving machine learning, in: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, 2017, pp. 1175\u20131191.","DOI":"10.1145\/3133956.3133982"},{"key":"10.1016\/j.knosys.2026.116040_b65","series-title":"Make Your Own Neural Network","author":"Rashid","year":"2016"},{"key":"10.1016\/j.knosys.2026.116040_b66","doi-asserted-by":"crossref","unstructured":"F. McSherry, K. Talwar, Mechanism design via differential privacy, in: 48th Annual IEEE Symposium on Foundations of Computer Science, FOCS\u201907, 2007, pp. 94\u2013103.","DOI":"10.1109\/FOCS.2007.66"},{"key":"10.1016\/j.knosys.2026.116040_b67","article-title":"Prototypical networks for few-shot learning","volume":"30","author":"Snell","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116040_b68","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.106631","article-title":"On the class overlap problem in imbalanced data classification","volume":"212","author":"Vuttipittayamongkol","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116040_b69","doi-asserted-by":"crossref","unstructured":"N. Gui, D. Ge, Z. Hu, AFS: An attention-based mechanism for supervised feature selection, in: Proceedings of the AAAI Conference on Artificial Intelligence, 2019, pp. 3705\u20133713.","DOI":"10.1609\/aaai.v33i01.33013705"},{"issue":"4","key":"10.1016\/j.knosys.2026.116040_b70","doi-asserted-by":"crossref","first-page":"305","DOI":"10.3390\/e20040305","article-title":"On the reduction of computational complexity of deep convolutional neural networks","volume":"20","author":"Maji","year":"2018","journal-title":"Entropy"},{"key":"10.1016\/j.knosys.2026.116040_b71","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1023\/A:1009953814988","article-title":"Automating the construction of internet portals with machine learning","volume":"3","author":"McCallum","year":"2000","journal-title":"Inf. Retr."},{"key":"10.1016\/j.knosys.2026.116040_b72","doi-asserted-by":"crossref","unstructured":"C.L. Giles, K.D. Bollacker, S. Lawrence, CiteSeer: An automatic citation indexing system, in: Proceedings of the Third ACM Conference on Digital Libraries, 1998, pp. 89\u201398.","DOI":"10.1145\/276675.276685"},{"key":"10.1016\/j.knosys.2026.116040_b73","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.future.2022.07.020","article-title":"BTG: A Bridge to Graph machine learning in telecommunications fraud detection","volume":"137","author":"Hu","year":"2022","journal-title":"Future Gener. Comput. Syst."},{"key":"10.1016\/j.knosys.2026.116040_b74","doi-asserted-by":"crossref","unstructured":"L. Tang, H. Liu, Relational learning via latent social dimensions, in: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009, pp. 817\u2013826.","DOI":"10.1145\/1557019.1557109"},{"key":"10.1016\/j.knosys.2026.116040_b75","doi-asserted-by":"crossref","unstructured":"B. Viswanath, A. Mislove, M. Cha, K.P. Gummadi, On the evolution of user interaction in facebook, in: Proceedings of the 2nd ACM Workshop on Online Social Networks, 2009, pp. 37\u201342.","DOI":"10.1145\/1592665.1592675"},{"key":"10.1016\/j.knosys.2026.116040_b76","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106646","article-title":"High-precision multiclass classification of lung disease through customized MobileNetV2 from chest X-ray images","volume":"155","author":"Shamrat","year":"2023","journal-title":"Comput. Biol. Med."}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126007665?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126007665?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,31]],"date-time":"2026-05-31T10:02:19Z","timestamp":1780221739000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126007665"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":76,"alternative-id":["S0950705126007665"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116040","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Privacy-preserving Federated Graph Neural Network with Global Semantic Augmentation and Layer-Wise Node Alignment for Social Analysis","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116040","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"116040"}}