A Survey: Network Feature Measurement Based on Machine Learning

Abstract

In network management, network measuring is crucial. Accurate network measurements can increase network utilization, network management, and the ability to find network problems promptly. With extensive technological advancements, the difficulty for network measurement is not just the growth in users and traffic but also the increasingly difficult technical problems brought on by the network’s design becoming more complicated. In recent years, network feature measurement issues have been extensively solved by the use of ML approaches, which are ideally suited to thorough data analysis and the investigation of complicated network behavior. However, there is yet no favored learning model that can best address the network measurement issue. The problems that ML applications in the field of network measurement must overcome are discussed in this study, along with an analysis of the current characteristics of ML algorithms in network measurement. Finally, network measurement techniques that have been used as ML techniques are examined, and potential advancements in the field are explored and examined.

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“…Machine learning algorithms can be trained on historical data to learn patterns of information dissemination within a given network, enabling the identification of influential nodes. Network science provides models that consider the complex structure of social networks, resulting in more accurate and efficient identification of influential nodes (Sun et al, 2023).…”