It takes two to tango: Exploring social tie development with both online and offline interactions

Peifeng Yin, Qi He, Xingjie Liu, Wang-chien Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Understanding social tie development among users is crucial for user engagement in social networking services. In this paper, we analyze the social interactions, both online and offline, of users and investigate the development of their social ties using data trail of "how social ties grow" left in mobile and social networking services. To the best of our knowledge, this is the first research attempt on studying social tie development by considering both online and offline interactions in a heterogeneous yet realistic relationship. In this study, we aim to answer three key questions: 1) is there a correlation between online and offline interactions? 2) how is the social tie developed via heterogeneous interaction channels? 3) would the development of social tie between two users be affected by their common friends? To achieve our goal, we develop a Social-aware Hidden Markov Model (SaHMM) that explicitly takes into account the factor of common friends in measure of the social tic development. Our experiments show that, comparing with results obtained using HMM and other heuristic methods, the social tie development captured by our SaHMM is significantly more consistent to lifetime profiles of users.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining 2014, SDM 2014
EditorsMohammed J. Zaki, Arindam Banerjee, Srinivasan Parthasarathy, Pang Ning-Tan, Zoran Obradovic, Chandrika Kamath
PublisherSociety for Industrial and Applied Mathematics Publications
Pages334-342
Number of pages9
ISBN (Electronic)9781510811515
DOIs
StatePublished - Jan 1 2014
Event14th SIAM International Conference on Data Mining, SDM 2014 - Philadelphia, United States
Duration: Apr 24 2014Apr 26 2014

Publication series

NameSIAM International Conference on Data Mining 2014, SDM 2014
Volume1

Other

Other14th SIAM International Conference on Data Mining, SDM 2014
CountryUnited States
CityPhiladelphia
Period4/24/144/26/14

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software

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