• DocumentCode
    251329
  • Title

    Community recommendation in social network using strong friends and quasi-clique approach

  • Author

    Matin, Anjum Ibna ; Jahan, Sawgath ; Huq, Mohammad Rezwanul

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Islamic Univ. of Technol., Gazipur, Bangladesh
  • fYear
    2014
  • fDate
    20-22 Dec. 2014
  • Firstpage
    453
  • Lastpage
    456
  • Abstract
    A social networking service is a platform to build relations among people who share interests, activities, backgrounds or real-life connections. Communities in a social network are the gathering places for the people with common interest. Social network analysis is in high demand now a days for the increasing number of users. They involve themselves into different communities. They share post, their views, what they like etc. in communities. So it is important for them to find suitable communities where they have common factors like friends, followers and their activities etc. In this paper, we propose a technique for recommending a community in social network like Facebook, Twitter etc. Finding strong friends from a user´s friend list and using clique and quasi-clique concepts introduced in graph mining, we recommend suitable communities for a user in a social network.
  • Keywords
    data mining; graph theory; recommender systems; social networking (online); community recommendation; graph mining; quasiclique approach; social networking service; Communities; Databases; Facebook; Forecasting; Lifting equipment; Positron emission tomography; clique; data mining; quasi-clique; social media; social network analysis and mining; strong friends;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electrical and Computer Engineering (ICECE), 2014 International Conference on
  • Conference_Location
    Dhaka
  • Print_ISBN
    978-1-4799-4167-4
  • Type

    conf

  • DOI
    10.1109/ICECE.2014.7026937
  • Filename
    7026937