The Network Completion Problem: Inferring Missing Nodes and Edges in Networks

While the social and information networks have become ubiquitous, the challenge of collecting complete network data still persists. Many times the collected network data is incomplete with nodes and edges missing. Commonly, only a part of the network can be observed and we would like to infer the unobserved part of the network. We address this issue by studying the Network Completion Problem: Given a network with missing nodes and edges, can we complete the missing part? We cast the problem in the Expectation Maximization (EM) framework where we use the observed part of the network to fit a model of network structure, and then we estimate the missing part of the network using the model, re-estimate the parameters and so on. We combine the EM with the Kronecker graphs model and design a scalable Metropolized Gibbs sampling approach that allows for the estimation of the model parameters as well as the inference about missing nodes and edges of the network. Experiments on synthetic and ...

• Type: Journal
• Year: 2011
• Where: SDM
• Authors: Myunghwan Kim 0002, Jure Leskovec
• Keywords: Data Mining, Expectation Maximization, Gibbs Sampling, Sampling Approach, SDM 2011