Finding friends on a new site using minimum information

Reza Zafarani, Huan Liu

Research output: Chapter in Book/Entry/PoemConference contribution

7 Scopus citations


With the emergence of numerous social media sites, individuals, with their limited time, often face a dilemma of choosing a few sites over others. Users prefer more engaging sites, where they can find familiar faces such as friends, relatives, or colleagues. Link prediction methods help find friends using link or content information. Unfortunately, whenever users join any site, they have no friends or any content generated. In this case, sites have no chance other than recommending random influential users to individuals hoping that users by befriending them create sufficient information for link prediction techniques to recommend meaningful friends. In this study, by considering social forces that form friendships, namely, influence, homophily, and confounding, and by employing minimum information available for users, we demonstrate how one can significantly improve random predictions without link or content information. In addition, contrary to the common belief that similarity between individuals is the essence of forming friendships, we show that it is the similarity that one exhibits to the friends of another individual that plays a more decisive role in predicting their future friendship.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining 2014, SDM 2014
EditorsMohammed Zaki, Zoran Obradovic, Pang Ning-Tan, Arindam Banerjee, Chandrika Kamath, Srinivasan Parthasarathy
PublisherSociety for Industrial and Applied Mathematics Publications
Number of pages9
ISBN (Electronic)9781510811515
StatePublished - 2014
Externally publishedYes
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


Other14th SIAM International Conference on Data Mining, SDM 2014
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

Cite this