Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Disambiguating authors in academic publications using random forests
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
Suggesting friends using the implicit social graph
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling link formation behaviors in dynamic social networks
SBP'11 Proceedings of the 4th international conference on Social computing, behavioral-cultural modeling and prediction
CollabSeer: a search engine for collaboration discovery
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Capturing missing edges in social networks using vertex similarity
Proceedings of the sixth international conference on Knowledge capture
Discovering missing links in networks using vertex similarity measures
Proceedings of the 27th Annual ACM Symposium on Applied Computing
The predictive value of young and old links in a social network
Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks
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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 CiteSeerX 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.