Structural inference for uncertain networks

TL;DR

This paper introduces a maximum-likelihood approach for community detection in uncertain networks, improving accuracy over thresholding methods and applicable to biological data.

Contribution

It develops a novel probabilistic method for inferring community structure from uncertain network data, enhancing accuracy over existing techniques.

Findings

Outperforms thresholding methods in reconstructing known communities

Successfully applied to protein-protein interaction networks

Demonstrates improved community detection accuracy on benchmark 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.