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Mining social applications network from business perspective using modularity maximization for community detection

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Abstract

There are different social applications available for different purposes. A lot of information about different fields including politics, sports, business, movie industry, etc., pass by and people are not well informed about most important happenings taking place in the world. Social applications usage varies among people in different parts of the world. A social application in a community may be popular for a particular purpose such as Twitter that may be used as a core application for political use among people in one part of the world, whereas other people may use Facebook, WeChat or YouTube for entertainment and other purposes and may not be aware of the important political changes taking place in the world. Social media usage by businesses can be improved by knowing the maximum usage of particular social applications among different communities of people so that targeted contents including information, advertisements, services and recommendations can be forwarded to them. In this paper, we mine social applications network by extracting knowledge according to the popularity of social applications. r-neighborhood technique is used for removal of edges from social applications network. Users are assigned to different communities based on the modularity scores. Optimal communities are found using divisive clustering approach that partitions the graph until maximum modularity score is achieved. Community detection method is also performed in gephi tool and using k-nearest neighbors graph. The trends of the social applications are analyzed among different communities, and it is seen that r-neighborhood, k-nearest neighbors and gephi tool result in Twitter, YouTube and Facebook as the most popular applications among other social applications. Related contents can be forwarded to the respective communities as well as people of a community defined by popularity of a social application can also be well informed about other happenings in the world such as Twitter and YouTube communities that may advertise about different products, whereas Facebook and YouTube communities are advertised with political news. The modularity function of k-nearest neighbors has the highest value and gives better interpretation of communities than other two techniques.

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Authors and Affiliations

  1. Xian Jiaotong University China, Xi’an, China

    Zeeshan Akbar & Jun Liu

  2. Center for Advanced Studies in Engineering (CASE), Islamabad, Pakistan

    Zahida Latif

Authors
  1. Zeeshan Akbar
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  2. Jun Liu
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  3. Zahida Latif
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Correspondence to Zeeshan Akbar.

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Akbar, Z., Liu, J. & Latif, Z. Mining social applications network from business perspective using modularity maximization for community detection. Soc. Netw. Anal. Min. 11, 115 (2021). https://doi.org/10.1007/s13278-021-00798-0

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  • DOI: https://doi.org/10.1007/s13278-021-00798-0

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