Predicting recent links in FOAF networks

Hung Hsuan Chen, Liang Gou, Xiaolong Zhang, C. Lee Giles

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

6 Scopus citations


For social networks, prediction of new links or edges can be important for many reasons, in particular for understanding future network growth. Recent work has shown that graph vertex similarity measures are good at predicting graph link formation for the near future, but are less effective in predicting further out. This could imply that recent links can be more important than older links in link prediction. To see if this is indeed the case, we apply a new relation strength similarity (RSS) measure on a coauthorship network constructed from a subset of the CiteSeer X dataset to study the power of recency. We choose RSS because it is one of the few similarity measures designed for weighted networks and easily models FOAF networks. By assigning different weights to the links according to authors coauthoring history, we show that recency is helpful in predicting the formation of new links.

Original languageEnglish (US)
Title of host publicationSocial Computing, Behavioral-Cultural Modeling and Prediction, 5th International Conference, SBP 2012, Proceedings
Number of pages8
StatePublished - 2012
Event5th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2012 - College Park, MD, United States
Duration: Apr 3 2012Apr 5 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7227 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other5th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2012
Country/TerritoryUnited States
CityCollege Park, MD

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


Dive into the research topics of 'Predicting recent links in FOAF networks'. Together they form a unique fingerprint.

Cite this