Network Reconstruction and Community Detection from Dynamics

Peixoto, Tiago P.

doi:10.1103/physrevlett.123.128301

Cited by 124 publications

(91 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The remaining task is to estimate the infection probabilities β ij . The goal of network inference (Peixoto 2019;Ma et al 2019;Di Lauro et al 2019;Timme and Casadiego 2014;Wang et al 2016) is to estimate the matrix B of infection probabilities from the SIR viral state observations…”

Section: Network Inferencementioning

confidence: 99%

Network-inference-based prediction of the COVID-19 epidemic outbreak in the Chinese province Hubei

Prasse

Achterberg

et al. 2020

Appl Netw Sci

View full text Add to dashboard Cite

At the moment of writing, the future evolution of the COVID-19 epidemic is unclear. Predictions of the further course of the epidemic are decisive to deploy targeted disease control measures. We consider a network-based model to describe the COVID-19 epidemic in the Hubei province. The network is composed of the cities in Hubei and their interactions (e.g., traffic flow). However, the precise interactions between cities is unknown and must be inferred from observing the epidemic. We propose the Network-Inference-Based Prediction Algorithm (NIPA) to forecast the future prevalence of the COVID-19 epidemic in every city. Our results indicate that NIPA is beneficial for an accurate forecast of the epidemic outbreak.

show abstract

Section: Network Inferencementioning

confidence: 99%

Network-inference-based prediction of the COVID-19 epidemic outbreak in the Chinese province Hubei

Prasse

Achterberg

et al. 2020

Appl Netw Sci

View full text Add to dashboard Cite

show abstract

“…For example, node degree as high degree nodes are potentially less likely to be influenced by a low degree node or some node similarity measure (are they 'politically' or 'socially' similar or produce similar textual content). Inferring some of these characteristics of nodes, such as their clustering, from information diffusion has recently been addressed in [37] and remains an area of future work. The dependence of social influence and information flow on the content of the information could also be investigated and incorporated.…”

Section: Discussionmentioning

confidence: 99%

“…Inferring β uv parameters that are dependent on the link itself. There have been a number of works [13,16,37] inferring the link strength between individuals as a measure of social influence or social trust using EM algorithms based on communication patterns. More recently, Bayesian methods have been applied in this space.…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, some approaches based on MLE use Bayesian techniques at intermediate steps to improve results [8]. Only recently have Bayesian techniques have been introduced to quantify the uncertainty of network structure inference from observation of dynamics [11,15,37,43]. Ghalebi et al highlight the need for general probabilistic frameworks for inference problems and propose the algorithm DYFERENCE that samples edge and node probabilities in an online algorithm for dynamic network inference [15].…”

Section: Background and Related Workmentioning

confidence: 99%

“…Ghalebi et al highlight the need for general probabilistic frameworks for inference problems and propose the algorithm DYFERENCE that samples edge and node probabilities in an online algorithm for dynamic network inference [15]. Peixoto [37] recently proposed the most closely related work in an algorithm designed to jointly reconstruct network structure and community labels assuming a stochastic block model structure and highlights the benefits of recovering the full posterior distribution of networks.…”

Section: Background and Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Bayesian Inference of Network Structure From Information Cascades

Gray

Mitchell

Roughan

2020

IEEE Trans. on Signal and Inf. Process. over Networks

View full text Add to dashboard Cite

Contagion processes are strongly linked to the network structures on which they propagate, and learning these structures is essential for understanding and intervention on complex network processes such as epidemics and (mis)information propagation. However, using contagion data to infer network structure is a challenging inverse problem. In particular, it is imperative to have appropriate measures of uncertainty in network structure estimates, however these are largely ignored in most machine-learning approaches. We present a probabilistic framework that uses samples from the distribution of networks that are compatible with the dynamics observed to produce network and uncertainty estimates. We demonstrate the method using the well known independent cascade model to sample from the distribution of networks P(G) conditioned on the observation of a set of infections C. We evaluate the accuracy of the method by using the marginal probabilities of each edge in the distribution, and show the benefits of quantifying uncertainty to improve estimates and understanding, particularly with small amounts of data.

show abstract

Identifying microbial drivers in biological phenotypes with a Bayesian network regression model

Ozminkowski,

Solís‐Lemus

2024

Ecology and Evolution

View full text Add to dashboard Cite

In Bayesian Network Regression models, networks are considered the predictors of continuous responses. These models have been successfully used in brain research to identify regions in the brain that are associated with specific human traits, yet their potential to elucidate microbial drivers in biological phenotypes for microbiome research remains unknown. In particular, microbial networks are challenging due to their high dimension and high sparsity compared to brain networks. Furthermore, unlike in brain connectome research, in microbiome research, it is usually expected that the presence of microbes has an effect on the response (main effects), not just the interactions. Here, we develop the first thorough investigation of whether Bayesian Network Regression models are suitable for microbial datasets on a variety of synthetic and real data under diverse biological scenarios. We test whether the Bayesian Network Regression model that accounts only for interaction effects (edges in the network) is able to identify key drivers (microbes) in phenotypic variability. We show that this model is indeed able to identify influential nodes and edges in the microbial networks that drive changes in the phenotype for most biological settings, but we also identify scenarios where this method performs poorly which allows us to provide practical advice for domain scientists aiming to apply these tools to their datasets. BNR models provide a framework for microbiome researchers to identify connections between microbes and measured phenotypes. We allow the use of this statistical model by providing an easy‐to‐use implementation which is publicly available Julia package at https://github.com/solislemuslab/BayesianNetworkRegression.jl.

show abstract

Network Reconstruction and Community Detection from Dynamics

Cited by 124 publications

References 45 publications

Network-inference-based prediction of the COVID-19 epidemic outbreak in the Chinese province Hubei

Network-inference-based prediction of the COVID-19 epidemic outbreak in the Chinese province Hubei

Bayesian Inference of Network Structure From Information Cascades

Identifying microbial drivers in biological phenotypes with a Bayesian network regression model

Contact Info

Product

Resources

About