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Visibility graph analysis of web server log files

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  • Sulaimany, Sadegh
  • Mafakheri, Aso

Abstract

Web servers store every event in the form of logs, which contain the retrieved URL, client IP address, access time, HTTP status code, etc. There are several useful methods to analyze these log files, which are mainly based on sequential text mining techniques for applications like prefetching or driving critical information about system’s security. Finding ways to convert web server log files into graph structures may open new horizons in complex network analysis for investigation, comparison, and prediction. Since visibility graph has various successful usages for analyzing different time series data, web server log files as a kind of time-series data have the potential to be converted into visibility graph. In this research, we propose a novel method to convert web server log files into horizontal visibility graphs. Afterward, we demonstrate the result of the method on two popular datasets, NASA and Online Judge web server log files, and perform exploratory and visibility graph analysis techniques like centrality measures computation and community detection to show the promising future for the research. Moreover, we introduce a novel algorithm for a common application in web server log file analysis, web prefetching, based on a modified version of link prediction on the extracted visibility graph, and evaluate it based on AUC assessment and propose the next page to prefetch in each dataset. Finally, we propose several choices to extend the research in case of technical and practical aspects.

Suggested Citation

  • Sulaimany, Sadegh & Mafakheri, Aso, 2023. "Visibility graph analysis of web server log files," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
    • Handle: RePEc:eee:phsmap:v:611:y:2023:i:c:s0378437123000031
    DOI: 10.1016/j.physa.2023.128448
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    References listed on IDEAS

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    1. Bhaduri, Anirban & Bhaduri, Susmita & Ghosh, Dipak, 2017. "Visibility graph analysis of heart rate time series and bio-marker of congestive heart failure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 786-795.
    2. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    3. Zhu, Jia & Wei, Daijun, 2021. "Analysis of stock market based on visibility graph and structure entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 576(C).
    4. Yu, Xuan & Shi, Suixiang & Xu, Lingyu & Yu, Jie & Liu, Yaya, 2020. "Analyzing dynamic association of multivariate time series based on method of directed limited penetrable visibility graph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    5. Cao, Run-Hua & Deng, Zheng-Hong & Xu, Ji-Wei, 2022. "Analysis of precipitation characteristics in Shanghai based on the visibility graph algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
    6. Tsiotas, Dimitrios & Charakopoulos, Avraam, 2018. "Visibility in the topology of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 280-292.
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    Cited by:

    1. Hu, Xiaohua & Niu, Min, 2023. "Horizontal visibility graphs mapped from multifractal trinomial measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).

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