Dimension reduction for covariates in network data

Summary A problem of major interest in network data analysis is to explain the strength of connections using context information. To achieve this, we introduce a novel approach, called network-supervised dimension reduction, in which covariates are projected onto low-dimensional spaces to reveal the...

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Bibliographic Details
Published in:Biometrika 2022-03, Vol.109 (1), p.85-102
Main Authors: Zhao, Junlong, Liu, Xiumin, Wang, Hansheng, Leng, Chenlei
Format: Article
Language:English
Subjects:
Astronomy
Clustering
Data analysis
Discriminant analysis
Graph coloring
Principal components analysis
Pulsars
Reduction
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Summary:Summary A problem of major interest in network data analysis is to explain the strength of connections using context information. To achieve this, we introduce a novel approach, called network-supervised dimension reduction, in which covariates are projected onto low-dimensional spaces to reveal the linkage pattern without assuming a model. We propose a new loss function for estimating the parameters in the resulting linear projection, based on the notion that closer proximity in the low-dimension projection corresponds to stronger connections. Interestingly, the convergence rate of our estimator is found to depend on a network effect factor, which is the smallest number that can partition a graph in a manner similar to the graph colouring problem. Our method has interesting connections to principal component analysis and linear discriminant analysis, which we exploit for clustering and community detection. The proposed approach is further illustrated by numerical experiments and analysis of a pulsar candidates dataset from astronomy.
ISSN:0006-3444
1464-3510
DOI:10.1093/biomet/asab006