The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
ACM SIGKDD Explorations Newsletter
Prediction and ranking algorithms for event-based network data
ACM SIGKDD Explorations Newsletter
Combining content and link for classification using matrix factorization
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Local Probabilistic Models for Link Prediction
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Non-negative Matrix Factorization on Manifold
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
The Time-Series Link Prediction Problem with Applications in Communication Surveillance
INFORMS Journal on Computing
Link Prediction on Evolving Data Using Matrix and Tensor Factorizations
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Supervised rank aggregation approach for link prediction in complex networks
Proceedings of the 21st international conference companion on World Wide Web
Feature selection for link prediction
Proceedings of the 5th Ph.D. workshop on Information and knowledge
Latent factor blockmodel for modelling relational data
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
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In this paper we address the problem of temporal link prediction, i.e., predicting the apparition of new links, in time-evolving networks. This problem appears in applications such as recommender systems, social network analysis or citation analysis. Link prediction in time-evolving networks is usually based on the topological structure of the network only. We propose here a model which exploits multiple information sources in the network in order to predict link occurrence probabilities as a function of time. The model integrates three types of information: the global network structure, the content of nodes in the network if any, and the local or proximity information of a given vertex. The proposed model is based on a matrix factorization formulation of the problem with graph regularization. We derive an efficient optimization method to learn the latent factors of this model. Extensive experiments on several real world datasets suggest that our unified framework outperforms state-of-the-art methods for temporal link prediction tasks.