TY - GEN
T1 - Predicting recent links in FOAF networks
AU - Chen, Hung Hsuan
AU - Gou, Liang
AU - Zhang, Xiaolong
AU - Giles, C. Lee
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84859147903&partnerID=8YFLogxK
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U2 - 10.1007/978-3-642-29047-3_19
DO - 10.1007/978-3-642-29047-3_19
M3 - Conference contribution
AN - SCOPUS:84859147903
SN - 9783642290466
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 156
EP - 163
BT - Social Computing, Behavioral-Cultural Modeling and Prediction, 5th International Conference, SBP 2012, Proceedings
T2 - 5th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2012
Y2 - 3 April 2012 through 5 April 2012
ER -