Network structure from rich but noisy data

Newman, M. E. J.

doi:10.1038/s41567-018-0076-1

Cited by 191 publications

(176 citation statements)

References 41 publications

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“…Furthermore, the peaked degree distribution of social interactions is transformed to a monotonic one if only * yohsuke.murase@gmail.com † KerteszJ@ceu.edu one communication channel is sampled [3]. Also, other network quantities such as degree correlations, centrality measures, and clustering properties, could undergo nontrivial bias effects depending on how networks are sampled [4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Thus, understanding the effect of sampling biases is crucial in interpreting empirical data better and in studying the original systems.…”

Section: Introductionmentioning

confidence: 99%

Sampling networks by nodal attributes

Murase

Török

et al. 2019

Phys. Rev. E

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In a social network individuals or nodes connect to other nodes by choosing one of the channels of communication at a time to re-establish the existing social links. Since available data sets are usually restricted to a limited number of channels or layers, these autonomous decision making processes by the nodes constitute the sampling of a multiplex network leading to just one (though very important) example of sampling bias caused by the behavior of the nodes. We develop a general setting to get insight and understand the class of network sampling models, where the probability of sampling a link in the original network depends on the attributes h of its adjacent nodes. Assuming that the nodal attributes are independently drawn from an arbitrary distribution ρ(h) and that the sampling probability r(hi, hj) for a link ij of nodal attributes hi and hj is also arbitrary, we derive exact analytic expressions of the sampled network for such network characteristics as the degree distribution, degree correlation, and clustering spectrum. The properties of the sampled network turn out to be sums of quantities for the original network topology weighted by the factors stemming from the sampling. Based on our analysis, we find that the sampled network may have samplinginduced network properties that are absent in the original network, which implies the potential risk of a naive generalization of the results of the sample to the entire original network. We also consider the case, when neighboring nodes have correlated attributes to show how to generalize our formalism for such sampling bias and we get good agreement between the analytic results and the numerical simulations.

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Section: Introductionmentioning

confidence: 99%

Sampling networks by nodal attributes

Murase

Török

et al. 2019

Phys. Rev. E

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“…Bayesian network reconstruction -We approach the network reconstruction task similarly to the situation where the network edges are measured directly, but via an uncertain process [33,34]: If D is the measurement of some process that takes place on a network, we can define a posterior distribution for the underlying adjacency matrix A via Bayes' rule,…”

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confidence: 99%

Network Reconstruction and Community Detection from Dynamics

Peixoto

2019

Phys. Rev. Lett.

124

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We present a scalable nonparametric Bayesian method to perform network reconstruction from observed functional behavior that at the same time infers the communities present in the network. We show that the joint reconstruction with community detection has a synergistic effect, where the edge correlations used to inform the existence of communities are also inherently used to improve the accuracy of the reconstruction which, in turn, can better inform the uncovering of communities. We illustrate the use of our method with observations arising from epidemic models and the Ising model, both on synthetic and empirical networks, as well as on data containing only functional information.P (A, b|D) = P (D|A)P (A|b)P (b) P (D) .(2)

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“…But as is true for any empirical scenario, network data is subject to observational errors: parts of the network might not have been recorded, and the parts that have might be wrong. Although this problem has been recognized in the past in several studies [2][3][4][5][6][7][8][9][10][11], the practice of ignoring measurement error is still mainstream, and robust methods to take it into account are underdeveloped. This is in no small part due to the fact that most available network data contain no quantitative error assessment information of any kind, thus preventing primary experimental uncertainties to be propagated up the chain of analysis.…”

Section: Introductionmentioning

confidence: 99%

Reconstructing Networks with Unknown and Heterogeneous Errors

Peixoto

2018

Phys. Rev. X

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The vast majority of network datasets contains errors and omissions, although this is rarely incorporated in traditional network analysis. Recently, an increasing effort has been made to fill this methodological gap by developing network reconstruction approaches based on Bayesian inference. These approaches, however, rely on assumptions of uniform error rates and on direct estimations of the existence of each edge via repeated measurements, something that is currently unavailable for the majority of network data. Here we develop a Bayesian reconstruction approach that lifts these limitations by not only allowing for heterogeneous errors, but also for single edge measurements without direct error estimates. Our approach works by coupling the inference approach with structured generative network models, which enable the correlations between edges to be used as reliable uncertainty estimates. Although our approach is general, we focus on the stochastic block model as the basic generative process, from which efficient nonparametric inference can be performed, and yields a principled method to infer hierarchical community structure from noisy data. We demonstrate the efficacy of our approach with a variety of empirical and artificial networks.

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Network structure from rich but noisy data

Cited by 191 publications

References 41 publications

Sampling networks by nodal attributes

Sampling networks by nodal attributes

Network Reconstruction and Community Detection from Dynamics

Reconstructing Networks with Unknown and Heterogeneous Errors

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