Contextual prediction of communication flow in social networks

Munmun De Choudhury, Hari Sundaram, Ajita John, Dorée Duncan Seligmann

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

Abstract

The paper develops a novel computational framework for predicting communication flow in social networks based on several contextual features. The problem is important because prediction of communication flow can impact timely sharing of specific information across a wide array of communities. We determine the intent to communicate and communication delay between users based on several contextual features in a social network corresponding to (a) neighborhood context, (b) topic context and (c) recipient context. The intent to communicate and communication delay are modeled as regression problems which are efficiently estimated using Support Vector Regression. We predict the intent and the delay, on an interval of time using past communication data. We have excellent prediction results on a real-world dataset from MySpace.com with an accuracy of 13-16%. We show that the intent to communicate is more significantly influenced by contextual factors compared to the delay.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007
Pages57-65
Number of pages9
DOIs
StatePublished - Dec 1 2007
Externally publishedYes
EventIEEE/WIC/ACM International Conference on Web Intelligence, WI 2007 - Silicon Valley, CA, United States
Duration: Nov 2 2007Nov 5 2007

Publication series

NameProceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007

Other

OtherIEEE/WIC/ACM International Conference on Web Intelligence, WI 2007
CountryUnited States
CitySilicon Valley, CA
Period11/2/0711/5/07

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications

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