@inproceedings{6f5cffc1d29443dfa6850f295cf60380,
title = "Predicting recent links in FOAF networks",
abstract = "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.",
keywords = "Coauthor Network Analysis, Graph Analysis, RSS, Relation Strength Similarity, Social Network Analysis, Vertex Similarity",
author = "Chen, {Hung Hsuan} and Liang Gou and Xiaolong Zhang and Giles, {C. Lee}",
year = "2012",
doi = "10.1007/978-3-642-29047-3_19",
language = "???core.languages.en_GB???",
isbn = "9783642290466",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "156--163",
booktitle = "Social Computing, Behavioral-Cultural Modeling and Prediction, 5th International Conference, SBP 2012, Proceedings",
note = "5th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2012 ; Conference date: 03-04-2012 Through 05-04-2012",
}