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There is increasing appetite for analysing populations of network data due to the fast-growing body of applications demanding such methods.
A whole brain fmri atlas generated via spatially constrained spectral clustering
Craddock, R. C., James, G. A., Holtzheimer III, P. E., Hu, X. P., and Mayberg, H. S. (2012) · 1928
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The structure of the nervous system of the nematode caenorhabditis elegans
White, J. G., Southgate, E., Thomson, J. N., and Brenner, S. (1986) · 1986
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A novel genetic system to detect protein–protein interactions
Fields, S. and Song, O.-k. (1989) · 1989
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Inference from coarse data via multiple imputation with application to age heaping
Heitjan, D. F. and Rubin, D. B. (1990) · 1990
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Ignorability and coarse data
Heitjan, D. F. and Rubin, D. B. (1991) · 1991
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On bayesian analysis of mixtures with an unknown number of components (with discussion)
Richardson, S. and Green, P. J. (1997) · 1997
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Consistent estimation of the order of mixture models
Keribin, C. (2000) · 2000
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Markov chain sampling methods for dirichlet process mixture models
Neal, R. M. (2000) · 2000
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Latent space approaches to social network analysis
Hoff, P. D., Raftery, A. E., and Handcock, M. S. (2002) · 2002
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Small-world brain networks
Bassett, D. S. and Bullmore, E. (2006) · 2006
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Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis
Kim, H. and Park, H. (2007) · 2007
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Linear regression and its inference on noisy network-linked data
Le, C. M. and Li, T. (2020) · 2007
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Clustering of time-course gene expression data using functional data analysis
Song, J. J., Lee, H.-J., Morris, J. S., and Kang, S. (2007) · 2007
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Random dot product graph models for social networks
Young, S. J. and Scheinerman, E. R. (2007) · 2007
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Introduction to information retrieval
Schütze, H., Manning, C. D., and Raghavan, P. (2008) · 2008
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Handling sparsity via the horseshoe
Carvalho, C. M., Polson, N. G., and Scott, J. G. (2009) · 2009
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Modeling social networks from sampled data
Handcock, M. S. and Gile, K. J. (2010) · 2010
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Network-based auto-probit modeling for protein function prediction
Jiang, X., Gold, D., and Kolaczyk, E. D. (2011) · 2011
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Decoding brain states from fmri connectivity graphs
Richiardi, J., Eryilmaz, H., Schwartz, S., Vuilleumier, P., and Van De Ville, D. (2011) · 2011
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Imputation for statistical inference with coarse data
Kim, J. K. and Hong, M. (2012) · 2012
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Pattern classification of large-scale functional brain networks: identification of informative neuroimaging markers for epilepsy
Zhang, J., Cheng, W., Wang, Z., Zhang, Z., Lu, W., Lu, G., and Feng, J. (2012) · 2012
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Estimating latent processes on a network from indirect measurements
Airoldi, E. M. and Blocker, A. W. (2013) · 2013
Cited alongside, same era.
Bayesian analysis for partially observed network data, missing ties, attributes and actors
Koskinen, J. H., Robins, G. L., Wang, P., and Pattison, P. E. (2013) · 2013
Cited alongside, same era.
Shape clustering: Common structure discovery
Shen, W., Wang, Y., Bai, X., Wang, H., and Latecki, L. J. (2013) · 2013
Cited alongside, same era.
Analysis of juggling data: Object oriented data analysis of clustering in acceleration functions
Lu, X. and Marron, J. (2014) · 2014
Cited alongside, same era.
An open science resource for establishing reliability and reproducibility in functional connectomics
Zuo, X.-N., Anderson, J. S., Bellec, P., Birn, R. M., Biswal, B. B., Blautzik, J., Breitner, J. C., Buckner, R. L., Calhoun, V. D., Castellanos, F. X., et al. (2014) · 2014
Cited alongside, same era.
Estimating network structure from unreliable measurements
Newman, M. E. (2018) · 2018
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The multiple random dot product graph model
Nielsen, A. M. and Witten, D. (2018) · 2018
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Reconstructing networks with unknown and heterogeneous errors
Peixoto, T. P. (2018) · 2018
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Optimal bayesian estimators for latent variable cluster models
Rastelli, R. and Friel, N. (2018) · 2018
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Tacita: A privacy preserving public display personalisation service
Shaw, P., Mikusz, M., Nurmi, P., and Davies, N. (2018) · 2018
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Network classification with applications to brain connectomics
Arroyo Relión, J. D., Kessler, D., Levina, E., and Taylor, S. F. (2019) · 2019
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Matrix estimation by universal singular value thresholding
Chatterjee, S. (2015) · 2015
Cited alongside, same era.
blockmodels: Latent and Stochastic Block Model Estimation by a ’V-EM’ Algorithm
INRA and Leger, J.-B. (2015) · 2015
Cited alongside, same era.
Utilizing covariates in partially observed networks
Marchette, D. J. and Hohman, E. L. (2015) · 2015
Cited alongside, same era.
Brain connectivity and novel network measures for alzheimer’s disease classification
Prasad, G., Joshi, S. H., Nir, T. M., Toga, A. W., Thompson, P. M., (ADNI, A. D. N. I., et al. (2015) · 2015
Cited alongside, same era.
Statistical inference on errorfully observed graphs
Priebe, C. E., Sussman, D. L., Tang, M., and Vogelstein, J. T. (2015) · 2015
Cited alongside, same era.
Joint modeling of multiple network views
Gollini, I. and Murphy, T. B. (2016) · 2016
Cited alongside, same era.
Model-based clustering based on sparse finite gaussian mixtures
Malsiner-Walli, G., Frühwirth-Schnatter, S., and Grün, B. (2016) · 2016
Cited alongside, same era.
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kmed: Distance-Based K-Medoids
Budiaji, W. (2019) · 2019
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Analysis of association football playing styles: An innovative method to cluster networks
Diquigiovanni, J. and Scarpa, B. (2019) · 2019
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From here to infinity: sparse finite versus dirichlet process mixtures in model-based clustering
Frühwirth-Schnatter, S. and Malsiner-Walli, G. (2019) · 2019
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Joint embedding of graphs
Wang, S., Arroyo, J., Vogelstein, J. T., and Priebe, C. E. (2019) · 2019
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Estimation of subgraph densities in noisy networks
Chang, J., Kolaczyk, E. D., and Yao, Q. (2020) · 2020
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Averages of unlabeled networks: Geometric characterization and asymptotic behavior
Kolaczyk, E. D., Lin, L., Rosenberg, S., Walters, J., and Xu, J. (2020) · 2020
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Model-based clustering for populations of networks
Signorelli, M. and Wit, E. C. (2020) · 2020
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Bayesian inference of network structure from unreliable data
Young, J.-G., Cantwell, G. T., and Newman, M. (2020) · 2020
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Inference for multiple heterogeneous networks with a common invariant subspace
Arroyo, J., Athreya, A., Cape, J., Chen, G., Priebe, C. E., and Vogelstein, J. T. (2021) · 2021
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Network recovery from unlabeled noisy samples
Josephs, N., Li, W., and Kolaczyk, E. D. (2021) · 2021
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Causal inference under network interference with noise
Li, W., Sussman, D. L., and Kolaczyk, E. D. (2021) · 2021
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Supplement to ”bayesian model-based clustering for populations of network data”
Mantziou, A., Lunagómez, S., and Mitra, R. (2023) · 2023
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Modeling network populations via graph distances
Lunagómez, S., Olhede, S. C., and Wolfe, P. J. (2021) · 2040
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On the propagation of low-rate measurement error to subgraph counts in large networks
Balachandran, P., Kolaczyk, E. D., and Viles, W. D. (2017) · 2057
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