# Network structure from rich but noisy data

## TL;DR

This paper introduces a method to accurately infer true network structures from complex, noisy data, addressing measurement errors that can distort naive estimates in social and biological networks.

## Contribution

It presents a novel technique for estimating true network structures from rich, multimodal data with significant measurement uncertainty.

## Key findings

- Effective estimation of true network structures from noisy data
- Application to social networks from face-to-face interactions and self-reported friendships
- Improved accuracy over naive network estimates

## Abstract

Driven by growing interest in the sciences, industry, and among the broader public, a large number of empirical studies have been conducted in recent years of the structure of networks ranging from the internet and the world wide web to biological networks and social networks. The data produced by these experiments are often rich and multimodal, yet at the same time they may contain substantial measurement error. In practice, this means that the true network structure can differ greatly from naive estimates made from the raw data, and hence that conclusions drawn from those naive estimates may be significantly in error. In this paper we describe a technique that circumvents this problem and allows us to make optimal estimates of the true structure of networks in the presence of both richly textured data and significant measurement uncertainty. We give example applications to two different social networks, one derived from face-to-face interactions and one from self-reported friendships.