Network Inference by Learned Node-Specific Degree Prior

TL;DR

This paper introduces a new network inference method that uses a learned node-specific degree prior to improve the accuracy of reconstructing networks from partial observations, outperforming previous approaches.

Contribution

The paper presents a novel degree prior derived from observed edges and formulates network inference as a regularized matrix completion problem, with theoretical and empirical validation.

Findings

The method improves network recovery error bounds.

Experimental results show superior performance on biological networks.

The degree prior adapts to observed network structures.

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.