Network Inference by Learned Node-Specific Degree Prior
This work addresses network inference for applications like biological networks, but it is incremental as it builds on existing matrix completion methods with a new regularization prior.
The paper tackles the problem of network inference from partially observed edges by proposing a method that uses a node-specific degree prior derived from observed data, formulated as a matrix completion problem. Experimental results show superior performance on simulated and real biological networks.
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.