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Будь ласка, використовуйте цей ідентифікатор, щоб цитувати або посилатися на цей матеріал: http://ds.knu.edu.ua/jspui/handle/123456789/9287
Назва: Edge of arXiv 2025: bibliometrics, themes, time trends, and networks
Автори: Semerikov, Serhiy O.
Ключові слова: edge computing
bibliometric study
arXiv preprints
thematic modelling
temporal trends
network communities
AI integration
federated learning
resource optimisation
IoT applications
emerging technologies
research gaps
Дата публікації: 21-лис-2025
Видавництво: Academy of Cognitive and Natural Sciences
Бібліографічний опис: Semerikov, S.O., 2025. Edge of arXiv 2025: bibliometrics, themes, time trends, and networks. Journal of Edge Computing [Online], 4(2), pp.116–158. Available from: https://doi.org/10.55056/jec.1214
Короткий огляд (реферат): This study conducts a comprehensive bibliometric, thematic, temporal, and network analysis of 2000 edge computing preprints published on arXiv during 2025. Drawing from a corpus authored by 8683 researchers across 124 categories, the analysis reveals a highly collaborative field with an average of 4.86 authors per paper. Thematic modelling identifies 10 core topics, led by energy-efficient computing (659 weighted occurrences), data management frameworks (608), and AI model deployment (509), while research types emphasise systems (28.2%), machine learning (20.0%), and theory (19.2%). Temporal patterns reveal consistent growth, averaging 333 papers per month, with peaks in July (434) and October (415), likely influenced by conference cycles. Network analysis reveals 1074 communities with a modularity of 0.847, highlighting specialised clusters in federated learning and AI at the edge, although security remains underrepresented (3.4%). The field demonstrates strong AI integration (91.25%) and identifies 292 emerging topics, signalling a rapid evolution toward sustainable, quantum-enhanced, and neuromorphic paradigms. Findings underscore gaps in security, real-world evaluation, and sustainability, while proposing directions for interdisciplinary advancement.
Опис: Boboris, K., 2025. Attention Authors: Updated Practice for Review Articles and Position Papers in arXiv CS Category. Available from: https://blog.arxiv.org/2025/10/31. Burbano, J.S., Abdullah, A., Zhantileuov, E., Liyanage, M. and Schuster, R., 2025. Dynamic Edge Server Selection in Time-Varying Environments: A Reliability-Aware Predictive Approach. Available from: https://doi.org/10.48550/arXiv.2511.10146. Chaban, O., Manziuk, E. and Radiuk, P., 2025. Method of adaptive knowledge distillation from multi-teacher to student deep learning models. Journal of Edge Computing, 4(2), pp.159–178. Available from: https://doi.org/10.55056/jec.978. DOI: https://doi.org/10.55056/jec.978 Chen, Y., Lu, J., Cao, S., Wang, W., Li, G. and Wen, G., 2025. FedCure: Mitigating Participation Bias in Semi-Asynchronous Federated Learning with Non-IID Data. Available from: https://doi.org/10.48550/arXiv.2511.10227. Cheong, C., Davis, G. and Choi, S., 2025. Weaver: Kronecker Product Approximations of Spatiotemporal Attention for Traffic Network Forecasting. Available from: https://doi.org/10.48550/arXiv.2511.08888. Guo, T., Zhao, S., Zhu, S. and Ma, C., 2025. SPEED-Q: Staged Processing with Enhanced Distillation towards Efficient Low-bit On-device VLM Quantization. Available from: https://doi.org/10.48550/arXiv.2511.08914. Ji, M., Souza, A.H. and Garg, V., 2025. On topological descriptors for graph products. Available from: https://doi.org/10.48550/arXiv.2511.08846. Jin, S., Jiang, Y., Liu, Y., Ma, T., Cao, D., Wei, L., Liu, X. and Zeng, X., 2025. DeepDR: an integrated deep-learning model web server for drug repositioning. Available from: https://doi.org/10.48550/arXiv.2511.08921. Konstantinidis, A.L., Papadopoulos, C. and Velissaris, G., 2025. Algorithms and Complexity of Hedge Cluster Deletion Problems. Available from: https://doi.org/10.48550/arXiv.2511.10202. Li, Z., Lin, C., Zheng, L., Wei, W.D., Liang, J. and Song, Q., 2025. GraphIF: Enhancing Multi-Turn Instruction Following for Large Language Models with Relation Graph Prompt. Available from: https://doi.org/10.48550/arXiv.2511.10051. Mehry, S. and Molaeinejad, M., 2025. The Homomorphism Submodule Graph. Available from: https://doi.org/10.48550/arXiv.2511.07837. Njoroge, T., Kibuku, R. and Mugoye, K., 2025. Comparative and edge-hybrid modeling of EfficientNetV2 and MobileNetV2 for multi-class crop disease classification with statistical validation. Journal of Edge Computing, 4(2), pp.234–262. Available from: https://doi.org/10.55056/jec.905. DOI: https://doi.org/10.55056/jec.905 Onuchin, A., Sorokin, K., Beketov, M. and Tupikina, L., 2025. Iterative Ricci-Foster Curvature Flow with GMM-Based Edge Pruning: A Novel Approach to Community Detection. Available from: https://doi.org/10.48550/arXiv.2511.08919. Ren, Y., Liu, Y., Zhou, Y., Zheng, Z., Shang, L., Yang, F. and Wang, Z., 2025. Bridging the Initialization Gap: A Co-Optimization Framework for Mixed-Size Global Placement. Available from: https://doi.org/10.48550/arXiv.2511.10073. Riabko, A.V., Vakaliuk, T.A., Zaika, O.V. and Kukharchuk, R.P., 2025. Acoustic Doppler localisation and tracking in 3D space with retardation correction. Journal of Edge Computing, 4(2), pp.263–287. Available from: https://doi.org/10.55056/jec.1201. DOI: https://doi.org/10.55056/jec.1201 Semerikov, S.O., Vakaliuk, T.A., Kanevska, O.B., Ostroushko, O.A. and Kolhatin, A.O., 2025. Edge intelligence unleashed: a survey on deploying large language models in resource-constrained environments. Journal of Edge Computing, 4(2), pp.179–233. Available from: https://doi.org/10.55056/jec.1000. DOI: https://doi.org/10.55056/jec.1000 Song, H. and Xu, H., 2025. Analytical estimations of edge states and extended states in large finite-size lattices. Available from: https://doi.org/10.48550/arXiv.2511.07875. Xu, H., Chen, Y. and Zhang, D., 2025. Beyond empirical models: Discovering new constitutive laws in solids with graph-based equation discovery. Available from: https://doi.org/10.48550/arXiv.2511.09906. Yao, Y., Yang, G., Xu, R., Tu, Y. and Mo, H., 2025. An Improved Dual-Attention Transformer-LSTM for Small-Sample Prediction of Modal Frequency and Actual Anchor Radius in Micro Hemispherical Resonator Design. Available from: https://doi.org/10.48550/arXiv.2511.08900.
URI (Уніфікований ідентифікатор ресурсу): https://doi.org/10.55056/jec.1214
http://ds.knu.edu.ua/jspui/handle/123456789/9287
ISSN: 2837-181X
Розташовується у зібраннях:Кафедра професійної та соціально-гуманітарної освіти

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