Journal of Engineering, Project, and Production Management, 2026, 16(3), 2025-208
Dynamic Network Infrastructure Management Using Graph Computation and Incremental Clustering
Associate Professor, Saxo Fintech College at Business College, Geely University, Chengdu, 641423, China, E-mail: wangljlingji@outlook.com
Project Management
Received September 9, 2025; revised October 30, 2025; accepted April 23, 2026
Available online May 4, 2026
Abstract: With the rapid development of big data and cloud computing, the scale of network infrastructure continues to expand. Dense equipment and frequent dynamic changes pose new challenges to traditional network management methods. Therefore, a network infrastructure management method that integrates graph computation and incremental clustering is proposed. A graph computation framework is constructed by combining graph sampling and aggregation methods with bidirectional long short-term memory networks, and an incremental clustering algorithm is introduced to achieve dynamic node recognition and region partitioning. The experimental results on the TensorFlow platform showed that the graph computation framework achieved 95.32% node classification accuracy and an F1 score of 0.95. The average clustering time of the incremental clustering algorithm was 1.68 seconds, and the intra-cluster variance was as low as 0.21, which was significantly better than that of traditional methods. In addition, this method achieved task completion rates of 95.28% to 98.25% and 95.71% to 99.33%, verifying its efficiency and robustness in dynamic network environments. The research provides flexible, real-time solutions for the intelligent management of complex network infrastructure, which has important theoretical and practical value.
Keywords: Deep residual capsule network, equipment, fault diagnosis, production, rolling bearing.
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Citation: Wang, L. (2026). Dynamic Network Infrastructure Management Using Graph Computation and Incremental Clustering. Journal of Engineering, Project, and Production Management, 16(3), 2025-208.
