Statistical Analysis of Multi-Relational Network Recovery

Statistical Analysis of Multi-Relational Network Recovery

In this paper, we develop asymptotic theories for a class of latent variable models for large-scale multi-relational networks. In particular, we establish consistency results and asymptotic error bounds for the (penalized) maximum likelihood estimators when the size of the network tends to infinity....

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Bibliographic Details
Main Authors: Zhi Wang, Xueying Tang, Jingchen Liu
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-10-01
Series:Frontiers in Applied Mathematics and Statistics
Subjects:
multi-relational network
knowledge graph completion
tail probability
risk
asymptotic analysis
non-asymptotic analysis
Online Access:https://www.frontiersin.org/article/10.3389/fams.2020.540225/full
Description
Summary:In this paper, we develop asymptotic theories for a class of latent variable models for large-scale multi-relational networks. In particular, we establish consistency results and asymptotic error bounds for the (penalized) maximum likelihood estimators when the size of the network tends to infinity. The basic technique is to develop a non-asymptotic error bound for the maximum likelihood estimators through large deviations analysis of random fields. We also show that these estimators are nearly optimal in terms of minimax risk.
ISSN:2297-4687

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