About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
6th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

Research Article

Multi-layered friendship modeling for location-based Mobile Social Networks

Download1107 downloads
Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.4108/ICST.MOBIQUITOUS2009.6828,
        author={Nan  Li and Guanling  Chen},
        title={Multi-layered friendship modeling for location-based Mobile Social Networks},
        proceedings={6th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services},
        publisher={IEEE},
        proceedings_a={MOBIQUITOUS},
        year={2009},
        month={11},
        keywords={},
        doi={10.4108/ICST.MOBIQUITOUS2009.6828}
    }
    
  • Nan Li
    Guanling Chen
    Year: 2009
    Multi-layered friendship modeling for location-based Mobile Social Networks
    MOBIQUITOUS
    IEEE
    DOI: 10.4108/ICST.MOBIQUITOUS2009.6828
Nan Li1,*, Guanling Chen1,*
  • 1: Department of Computer Science, University of Massachusetts Lowell, 1 University Avenue, Lowell, MA 01854.
*Contact email: nli@cs.uml.edu, glchen@cs.uml.edu

Abstract

Location-based Mobile Social Networks (MSNs) are becoming increasingly popular given the success of Online Social Networks (OSNs), such as Facebook and MySpace, and recent availability of open mobile platforms, such as Apple iPhones and Google Android phones. MSNs extend existing OSNs by allowing a user to know when her friends are around and by providing the ability to meet new people who share her interests. There are few studies, however, on how users are connected through these emerging location-based MSNs. In this paper, we present analysis results of a commercial MSN for which we quantified the correlation between users' friendship with their mobility characteristics, social graph properties, and user profiles. The evaluation of the derived model from the empirical traces suggests that the model-based friend recommendation is effective, and its performance is better than well-known Naive Bayes classifier and J48 decision tree algorithms. To the best of our knowledge, this paper presents the first study that models the friendship connections over a real-world location-based MSN.

Published
2009-11-10
Publisher
IEEE
http://dx.doi.org/10.4108/ICST.MOBIQUITOUS2009.6828
Copyright © 2009–2025 ICST
EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL