  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.
