Finding others online: reputation systems for social online spaces
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
IEEE Transactions on Knowledge and Data Engineering
Social matching: A framework and research agenda
ACM Transactions on Computer-Human Interaction (TOCHI)
Collaboration over time: characterizing and modeling network evolution
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
The structure of information pathways in a social communication network
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Local Probabilistic Models for Link Prediction
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
SoRec: social recommendation using probabilistic matrix factorization
Proceedings of the 17th ACM conference on Information and knowledge management
Do you know?: recommending people to invite into your social network
Proceedings of the 14th international conference on Intelligent user interfaces
Make new friends, but keep the old: recommending people on social networking sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Predicting tie strength with social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Patterns and dynamics of users' behavior and interaction: Network analysis of an online community
Journal of the American Society for Information Science and Technology
Learning spectral graph transformations for link prediction
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
On social networks and collaborative recommendation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
On the evolution of user interaction in Facebook
Proceedings of the 2nd ACM workshop on Online social networks
Personalized recommendation of social software items based on social relations
Proceedings of the third ACM conference on Recommender systems
FriendSensing: recommending friends using mobile phones
Proceedings of the third ACM conference on Recommender systems
A social recommendation framework based on multi-scale continuous conditional random fields
Proceedings of the 18th ACM conference on Information and knowledge management
Towards time-aware link prediction in evolving social networks
Proceedings of the 3rd Workshop on Social Network Mining and Analysis
Modeling relationship strength in online social networks
Proceedings of the 19th international conference on World wide web
Suggesting ghost edges for a smaller world
Proceedings of the 20th ACM international conference on Information and knowledge management
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Online social networking platforms have become a popular channel of communications among people. However, most people can only keep in touch with a limited number of friends. This phenomenon results in a low-connectivity social network in terms of communications, which is inefficient for information propagation and social engagement. In this paper, we introduce a new recommendation service, called link revival, that suggests users to re-connect with their old friends, such that the resulted connection will improve the social network connectivity. To achieve high connectivity improvement under the dynamic social network evolvement, we propose a graph prediction-based recommendation strategy, which selects proper candidates based on the prediction of their future behaviors. We then develop an effective model that exploits non-homogeneous Poisson process and second-order self-similarity in prediction. Through comprehensive experimental studies on two real datasets (Phone Call Network and Facebook Wall-posts), we demonstrate that our proposed approach can significantly increase the social network connectivity, and that the approach outperforms other baseline solutions. The results also show that our solution is more suitable for online social networks like Facebook, partially due to the stronger long range dependency and lower communication costs in the interactions.