Scalable Temporal Latent Space Inference for Link Prediction in Dynamic Social Networks

We propose a temporal latent space model for link prediction in dynamic social networks, where the goal is to predict links over time based on a sequence of previous graph snapshots. The model assumes that each user lies in an unobserved latent space, and interactions are more likely to occur betwee...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2016-10, Vol.28 (10), p.2765-2777
Main Authors: Zhu, Linhong, Guo, Dong, Yin, Junming, Steeg, Greg Ver, Galstyan, Aram
Format: Article
Language:English
Subjects:
Calligraphy
Computational efficiency
Computational modeling
Electronic mail
Energy management
Heuristic algorithms
Latent space model
link prediction
non-negative matrix factorization
Prediction algorithms
Predictive models
social network analysis
Social network services
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We propose a temporal latent space model for link prediction in dynamic social networks, where the goal is to predict links over time based on a sequence of previous graph snapshots. The model assumes that each user lies in an unobserved latent space, and interactions are more likely to occur between similar users in the latent space representation. In addition, the model allows each user to gradually move its position in the latent space as the network structure evolves over time. We present a global optimization algorithm to effectively infer the temporal latent space. Two alternative optimization algorithms with local and incremental updates are also proposed, allowing the model to scale to larger networks without compromising prediction accuracy. Empirically, we demonstrate that our model, when evaluated on a number of real-world dynamic networks, significantly outperforms existing approaches for temporal link prediction in terms of both scalability and predictive power.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2016.2591009