Detecting web communities in attributed internet networks using a mathematical programming approach

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Alinezhad Esmaeil; Teimourpour Babak

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Journal: IRANIAN JOURNAL OF INFORMATION MANAGEMENT Year:2019 | Volume:4 | Issue:2 (7) Page(s): 1-20

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Title

Detecting web communities in attributed internet networks using a mathematical programming approach

Author(s)

Alinezhad Esmaeil | Teimourpour Babak | Issue Writer Certificate

Keywords

Community detectionQ1

Internet networkQ1

Mathematical modelQ1

Modularity optimizationQ1

Network topologyQ2

Node attributesQ1

Web pagesQ1

Abstract

Community detection is one of the emerging and well-known topics in the area of data mining and social network analysis, which has wide variety applications in discovering communities in real-world networks such as biological networks, internet weblogs, scientific and research websites, etc. Web Community detection can especially help admins assign the optimal bandwidth to the websites of their own networks. Most of web Community detection approaches only use the Network topology to discover the web communities. However, the results of the most recent researches show that traditional Community detection methods have to be substantially modified to consider web attributes as well as Network topology. Therefore, in this paper, a mathematical programming approach is developed for Community detection in attributed Internet networks by simultaneously considering both Network topology and Node attributes. In this approach, first, similarities of Web pages are calculated using Node attributes and a desired similarity measure and are considered as the weight of the corresponding edges. Then, communities of the resulted weighted network will be detected by the proposed Mathematical model. To validate and prove the efficiency, it is hypothesized that the detected communities of the proposed approach have a better quality than that of previous models. Experimental results demonstrate that the proposed approach has the ability to significantly improve the quality of detected web communities, when the model uses the Jaccard index. However, the results of other hypotheses indicate that the correct selection of similarity measure has a significant impact on the quality of the detected communities.

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APA: Copy

Alinezhad, Esmaeil, & Teimourpour, Babak. (2019). Detecting web communities in attributed internet networks using a mathematical programming approach. IRANIAN JOURNAL OF INFORMATION MANAGEMENT, 4(2 (7) ), 1-20. SID. https://sid.ir/paper/262651/en

Vancouver: Copy

Alinezhad Esmaeil, Teimourpour Babak. Detecting web communities in attributed internet networks using a mathematical programming approach. IRANIAN JOURNAL OF INFORMATION MANAGEMENT[Internet]. 2019;4(2 (7) ):1-20. Available from: https://sid.ir/paper/262651/en

IEEE: Copy

Esmaeil Alinezhad, and Babak Teimourpour, “Detecting web communities in attributed internet networks using a mathematical programming approach,” IRANIAN JOURNAL OF INFORMATION MANAGEMENT, vol. 4, no. 2 (7) , pp. 1–20, 2019, [Online]. Available: https://sid.ir/paper/262651/en

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