Abstract | ||
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In this paper, we try to solve the problem of temporal link prediction in information networks. This implies predicting the time it takes for a link to appear in the future, given its features that have been extracted at the current network snapshot. To this end, we introduce a probabilistic non-parametric approach, called Non-Parametric Generalized Linear Model (NP-GLM), which infers the hidden underlying probability distribution of the link advent time given its features. We then present a learning algorithm for NP-GLM and an inference method to answer time-related queries. Extensive experiments conducted on both synthetic data and real-world Sina Weibo social network demonstrate the effectiveness of NP-GLM in solving temporal link prediction problem vis-a-vis competitive baselines. |
Year | Venue | Field |
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2017 | arXiv: Learning | Social network,Inference,Nonparametric statistics,Synthetic data,Probability distribution,Generalized linear model,Artificial intelligence,Probabilistic logic,Snapshot (computer storage),Machine learning,Mathematics |
DocType | Volume | Citations |
Journal | abs/1706.06783 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Sina Sajadmanesh | 1 | 15 | 1.95 |
Jiawei Zhang | 2 | 806 | 72.17 |
Hamid R. Rabiee | 3 | 336 | 41.77 |