Structural inference for uncertain networks

Abstract

In the study of networked systems such as biological, technological, and social networks the available data are often uncertain. Rather than knowing the structure of a network exactly, we know the connections between nodes only with a certain probability. In this paper we develop methods for the analysis of such uncertain data, focusing particularly on the problem of community detection. We give a principled maximum-likelihood method for inferring community structure and demonstrate how the results can be used to make improved estimates of the true structure of the network. Using computer-generated benchmark networks we demonstrate that our methods are able to reconstruct known communities more accurately than previous approaches based on data thresholding. We also give an example application to the detection of communities in a protein-protein interaction network.

12 pages, 4 figures

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Cited by in corpus (12)

• Network structure from rich but noisy data
• Bayesian stochastic blockmodeling
• Estimating network structure from unreliable measurements
• Reconstructing networks with unknown and heterogeneous errors
• Bayesian inference of network structure from unreliable data
• Descriptive vs. inferential community detection in networks: pitfalls, myths, and half-truths
• A statistical model for brain networks inferred from large-scale electrophysiological signals
• Mapping flows on sparse networks with missing links
• Mapping flows on weighted and directed networks with incomplete observations
• Edge Proposal Sets for Link Prediction
• Community models for networks observed through edge nominations
• Detecting new edge types in a temporal network model