Network Inference by Learned Node-Specific Degree Prior

Qingming Tang; Lifu Tu; Weiran Wang; Jinbo Xu

doi:10.48550/arxiv.1602.02386

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Network Inference by Learned Node-Specific Degree Prior

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Network Inference by Learned Node-Specific Degree Prior

Qingming Tang, Lifu Tu, Weiran Wang and Jinbo Xu

arXiv (Cornell University)

02/07/2016

DOI: 10.48550/arxiv.1602.02386

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https://doi.org/10.48550/arXiv.1602.02386View

Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

We propose a novel method for network inference from partially observed edges using a node-specific degree prior. The degree prior is derived from observed edges in the network to be inferred, and its hyper-parameters are determined by cross validation. Then we formulate network inference as a matrix completion problem regularized by our degree prior. Our theoretical analysis indicates that this prior favors a network following the learned degree distribution, and may lead to improved network recovery error bound than previous work. Experimental results on both simulated and real biological networks demonstrate the superior performance of our method in various settings.

Computer Science - Learning

Statistics - Machine Learning

Details

Title: Subtitle: Network Inference by Learned Node-Specific Degree Prior
Creators: Qingming Tang
Lifu Tu
Weiran Wang
Jinbo Xu
Resource Type: Preprint
Publication Details: arXiv (Cornell University)
DOI: 10.48550/arxiv.1602.02386
eISSN: 2331-8422
Language: English
Date posted: 02/07/2016
Academic Unit: Computer Science
Record Identifier: 9984696872302771

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