C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Link mining: a new data mining challenge
ACM SIGKDD Explorations Newsletter
ACM SIGKDD Explorations Newsletter
Introduction to Information Retrieval
Introduction to Information Retrieval
Local Probabilistic Models for Link Prediction
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Semantic and Event-Based Approach for Link Prediction
PAKM '08 Proceedings of the 7th International Conference on Practical Aspects of Knowledge Management
On the evolution of user interaction in Facebook
Proceedings of the 2nd ACM workshop on Online social networks
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Towards time-aware link prediction in evolving social networks
Proceedings of the 3rd Workshop on Social Network Mining and Analysis
Using Abstract Information and Community Alignment Information for Link Prediction
ICMLC '10 Proceedings of the 2010 Second International Conference on Machine Learning and Computing
New perspectives and methods in link prediction
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Link prediction with social vector clocks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Link prediction in social networks such as collaboration networks and friendship networks have recently attracted a great deal of attention. There have been numerous attempts to address this problem through diverse approaches. In the present paper, we focus on the temporal behavior of the link strength, particularly the relationship between the time stamps of interactions or links and the temporal behavior of link strength and how link strength affects future link evolution. Most of the previous studies neglected the impact of time stamps of the interactions and of the links on link evolution. The gap between the current time and the time stamps of the interactions or links is also important to link evolution. In the present paper, we introduced a new time aware index, referred to as time score, that captures the important aspects of time stamps of interactions and the temporality of the link strengths. We apply time score to two social network data sets, namely, a coauthorship network data set and a Facebook friendship network data set. The results reveal a significant improvement in predicting future links.