Bayesian classification, anomaly detection, and survival analysis using network inputs with application to the microbiome

Josephs, Nathaniel; Lin, Lizhen; Rosenberg, Steven A.; Kolaczyk, Eric D.

doi:10.48550/arxiv.2004.04765

Cited by 1 publication

(2 citation statements)

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“…MB networks can be constructed from such data, where nodes correspond to taxa and edges represent the co-occurrence of taxa in the corresponding OTUs. We use the networks constructed in Josephs et al (2020) and note that network analysis is an emerging tool in the MB literature. 5.2.…”

Section: Real Data Examplesmentioning

confidence: 99%

“…Supervised and unsupervised learning such as clustering, regression, and classification for network objects have also been considered in the literature. See, e.g., Arroyo Relión et al (2019) and Josephs et al (2020), with the former considering network classification in neuroimaging and the latter employing Bayesian methods for classification, anomaly detection, and survival analysis.…”

Section: Introductionmentioning

confidence: 99%

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A spectral-based framework for hypothesis testing in populations of networks

Chen¹,

Josephs²,

Lin³

et al. 2020

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In this paper, we propose a new spectral-based approach to hypothesis testing for populations of networks. The primary goal is to develop a test to determine whether two given samples of networks come from the same random model or distribution. Our test statistic is based on the trace of the third order for a centered and scaled adjacency matrix, which we prove converges to the standard normal distribution as the number of nodes tends to infinity. The asymptotic power guarantee of the test is also provided. The proper interplay between the number of networks and the number of nodes for each network is explored in characterizing the theoretical properties of the proposed testing statistics. Our tests are applicable to both binary and weighted networks, operate under a very general framework where the networks are allowed to be large and sparse, and can be extended to multiple-sample testing. We provide an extensive simulation study to demonstrate the superior performance of our test over existing methods and apply our test to three real datasets.

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Section: Real Data Examplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%