Network Metric System and Scenario-Differentiated Analysis Driven by LLM Literature Mining

XU Qikun; LIU Yaxi; HAN Shuxian; ZHANG Huifeng; HUANGFU Wei

doi:10.11999/JEIT251120

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XU Qikun, LIU Yaxi, HAN Shuxian, ZHANG Huifeng, HUANGFU Wei. Network Metric System and Scenario-Differentiated Analysis Driven by LLM Literature Mining[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251120

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XU Qikun, LIU Yaxi, HAN Shuxian, ZHANG Huifeng, HUANGFU Wei. Network Metric System and Scenario-Differentiated Analysis Driven by LLM Literature Mining[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251120

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Network Metric System and Scenario-Differentiated Analysis Driven by LLM Literature Mining

doi: 10.11999/JEIT251120 cstr: 32379.14.JEIT251120

1.
School of Computer & Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
Zhejiang Lab, Hangzhou 311121, China

Funds: The National Key Research and Development Program of China (2023YFB2903901)

Received Date: 2025-10-23
Accepted Date: 2026-06-29
Rev Recd Date: 2026-06-29

Available Online: 2026-07-08

Abstract

Abstract

Objective Network metrics provide the foundation for network design, operation, and optimization. Existing studies primarily focus on individual scenarios or representative metrics and lack unified extraction rules and reproducible workflows for large-scale, cross-scenario metric analysis. To address terminology ambiguity, scenario heterogeneity, and the quantification of complex metric relationships, this study proposes a reproducible domain-specific literature mining framework based on a Large Language Model (LLM). The framework automatically extracts and standardizes network metrics, annotates application scenarios, quantifies inter-metric relationships, and establishes a Service-Multiplexing-Versatility (SMV) analytical framework. Rather than providing a complete set of metric calculation methods, the SMV framework serves as a conceptual model for guiding multi-objective tradeoffs in network architecture design and lifecycle management. Methods An automated literature mining framework based on a multi-agent LLM architecture is developed (Fig. 1). A dataset comprising 583 articles published in IEEE/ACM Transactions on Networking during 2023–2024 is analyzed. The framework consists of three specialized agents. A terminology normalization agent maps aliases and synonymous expressions to standardized metric names. A scenario annotation agent assigns primary application scenario labels using high-information-density sections of each article. A correlation mining agent identifies the semantic direction and strength of relationships between metric pairs and quantifies these relationships as signed correlation coefficients ranging from −1 to +1. The reliability of the mining results is evaluated through dual-LLM cross-validation and manual sampling review (Fig. 2). Results and Discussions The proposed framework extracts 3,978 independent network metrics, of which 138 appear in more than 1% of the analyzed articles (Fig. 3). The metric frequency distribution exhibits a pronounced heavy-tailed distribution, with throughput (79.1%), end-to-end delay (74.6%), and packet error rate (59.5%) representing the most frequently studied metrics (Table 1). The core metric sets show strong scenario dependence (Fig. 4). For example, data center networks primarily emphasize throughput, end-to-end delay, and flow completion time, whereas Internet of Things (IoT) applications additionally prioritize energy consumption and network lifetime (Table 2). Furthermore, scenario-specific correlation matrices reveal markedly different coupling patterns among metrics (Figs. 5 and 6). In data center networks, throughput is strongly negatively correlated with flow completion time and queueing delay, reflecting the fundamental tradeoff associated with congestion control. In edge computing networks, end-to-end delay is negatively correlated with resource utilization, indicating the balance between real-time task offloading and resource utilization. Conclusions The strong coupling between network metrics and application scenarios indicates that future network architectures should be evaluated from a multidimensional perspective. Based on the extracted scenario-specific metric relationships, this study proposes the SMV analytical framework (Fig. 7). By jointly considering differentiated service quality requirements (Service), physical infrastructure cost and resource reuse (Multiplexing), and adaptive reconfiguration capability for emerging services and application scenarios (Versatility), the framework provides a theoretical basis for adaptive resource orchestration in AI-native networks. Future work will extend the current static literature mining pipeline into a continuously updated network metric knowledge base and further validate the engineering applicability of the SMV framework in programmable and multimodal networks.
- Network metric,
- Large Language Model (LLM),
- Metric system,
- Multimodal network

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References(26)

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