Machine Learning
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental graph pattern matching
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Discovering missing links in networks using vertex similarity measures
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Predicting recent links in FOAF networks
SBP'12 Proceedings of the 5th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Towards trust inference from bipartite social networks
DBSocial '12 Proceedings of the 2nd ACM SIGMOD Workshop on Databases and Social Networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Recent studies show that vertex similarity measures are good at predicting link formation over the near term, but are less effective in predicting over the long term. This indicates that, generally, as links age, their degree of influence diminishes. However, few papers have systematically studied this phenomenon. In this paper, we apply a supervised learning approach to study age as a factor for link formation. Experiments on several real-world datasets show that younger links are more informative than older ones in predicting the formation of new links. Since older links become less useful, it might be appropriate to remove them when studying network evolution. Several previously observed network properties and network evolution phenomena, such as "the number of edges grows super-linearly in the number of nodes" and "the diameter is decreasing as the network grows", may need to be reconsidered under a dynamic network model where old, inactive links are removed.