The Relationship between Online Social Network Ties and User Attributes

被引:14
作者
Mahmoudi, Amin [1 ,2 ]
Yaakub, Mohd Ridzwan [1 ,2 ]
Abu Bakar, Azuraliza [1 ,2 ]
机构
[1] Univ Kebangsaan Malaysia, Fac Informat Sci & Technol, Bangi, Malaysia
[2] Univ Kebangsaan Malaysia, Sentiment Anal Lab, Ukm Bangi 43600, Selangor, Malaysia
关键词
Distance; online social network; tie formation; IO-fraction; user weight;
D O I
10.1145/3314204
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The distance between users has an effect on the formation of social network ties, but it is not the only or even the main factor. Knowing all the features that influence such ties is very important for many related domains such as location-based recommender systems and community and event detection systems for online social networks (OSNs). In recent years, researchers have analyzed the role of user geo-location in OSNs. Researchers have also attempted to determine the probability of friendships being established based on distance, where friendship is not only a function of distance. However, some important features of OSNs remain unknown. In order to comprehensively understand the OSN phenomenon, we also need to analyze users' attributes. Basically, an OSN functions according to four main user properties: user geo-location, user weight, number of user interactions, and user lifespan. The research presented here sought to determine whether the user mobility pattern can be used to predict users' interaction behavior. It also investigated whether, in addition to distance, the number of friends ( known as user weight) interferes in social network tie formation. To this end, we analyzed the above-stated features in three large-scale OSNs. We found that regardless of a high degree freedom in user mobility, the fraction of the number of outside activities over the inside activity is a significant fraction that helps us to address the user interaction behavior. To the best of our knowledge, research has not been conducted elsewhere on this issue. We also present a high-resolution formula in order to improve the friendship probability function.
引用
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页数:15
相关论文
共 26 条
  • [11] Kaltenbrunner A., 2012, Proceedings of the 2012 ACM workshop on Workshop on online social networks, P19
  • [12] SPOT: Locating Social Media Users Based on Social Network Context
    Kong, Longbo
    Liu, Zhi
    Huang, Yan
    [J]. PROCEEDINGS OF THE VLDB ENDOWMENT, 2014, 7 (13): : 1681 - 1684
  • [13] Geographical dispersal of mobile communication networks
    Lambiotte, Renaud
    Blondel, Vincent D.
    de Kerchove, Cristobald
    Huens, Etienne
    Prieur, Christophe
    Smoreda, Zbigniew
    Van Dooren, Paul
    [J]. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2008, 387 (21) : 5317 - 5325
  • [14] Geographies of an Online Social Network
    Lengyel, Balazs
    Varga, Attila
    Sagvari, Bence
    Jakobi, Akos
    Kertesz, Janos
    [J]. PLOS ONE, 2015, 10 (09):
  • [15] Leskovec J., 2005, 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, KDD'05, P177
  • [16] Geographic routing in social networks
    Liben-Nowell, D
    Novak, J
    Kumar, R
    Raghavan, P
    Tomkins, A
    [J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2005, 102 (33) : 11623 - 11628
  • [17] Liu Zhi, 2014, P SIGSPATIAL C, P525
  • [18] A new method to discretize time to identify the milestones of online social networks
    Mahmoudi A.
    Yaakub M.R.
    Abu Bakar A.
    [J]. Social Network Analysis and Mining, 2018, 8 (1)
  • [19] New time-based model to identify the influential users in online social networks
    Mahmoudi, Amin
    Yaakub, Mohd Ridzwan
    Abu Bakar, Azuraliza
    [J]. DATA TECHNOLOGIES AND APPLICATIONS, 2018, 52 (02) : 278 - 290
  • [20] LARS*: An Efficient and Scalable Location-Aware Recommender System
    Sarwat, Mohamed
    Levandoski, Justin J.
    Eldawy, Ahmed
    Mokbel, Mohamed F.
    [J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2014, 26 (06) : 1384 - 1399