Michael T. Schaub
Michael T. Schaub
RWTH Aachen University
Dirección de correo verificada de mit.edu - Página principal
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Citado por
SC3: consensus clustering of single-cell RNA-Seq data
VY Kiselev, K Kirschner, MT Schaub, T Andrews, A Yiu, T Chandra, ...
Nature Methods 14 (5), 483-486, 2017
Markov dynamics as a zooming lens for multiscale community detection: non clique-like communities and the field-of-view limit
MT Schaub, JC Delvenne, SN Yaliraki, M Barahona
PloS one 7 (2), e32210, 2012
Simplicial closure and higher-order link prediction
AR Benson, R Abebe, MT Schaub, A Jadbabaie, J Kleinberg
Proceedings of the National Academy of Sciences 115 (48), E11221-E11230, 2018
The many facets of community detection in complex networks
MT Schaub, JC Delvenne, M Rosvall, R Lambiotte
Applied Network Science 2 (1), 4, 2017
Graph partitions and cluster synchronization in networks of oscillators
MT Schaub, N O'Clery, YN Billeh, JC Delvenne, R Lambiotte, M Barahona
Chaos: An Interdisciplinary Journal of Nonlinear Science 26 (9), 094821, 2016
The stability of a graph partition: A dynamics-based framework for community detection
JC Delvenne, MT Schaub, SN Yaliraki, M Barahona
Dynamics On and Of Complex Networks, Volume 2, 221-242, 2013
Random walks on simplicial complexes and the normalized hodge 1-laplacian
MT Schaub, AR Benson, P Horn, G Lippner, A Jadbabaie
SIAM Review 62 (2), 353-391, 2020
Encoding dynamics for multiscale community detection: Markov time sweeping for the map equation
MT Schaub, R Lambiotte, M Barahona
Physical Review E 86 (2), 026112, 2012
Prediction of allosteric sites and mediating interactions through bond-to-bond propensities
BRC Amor, MT Schaub, SN Yaliraki, M Barahona
Nature Communications 7 (12477), 2016
Centrality measures for graphons: Accounting for uncertainty in networks
M Avella-Medina, F Parise, M Schaub, S Segarra
IEEE Transactions on Network Science and Engineering, 2018
Using higher-order Markov models to reveal flow-based communities in networks
V Salnikov, MT Schaub, R Lambiotte
Scientific reports 6 (1), 1-13, 2016
Structure of complex networks: Quantifying edge-to-edge relations by failure-induced flow redistribution
MT Schaub, J Lehmann, SN Yaliraki, M Barahona
Network Science 2 (1), 66-89, 2014
Emergence of Slow-Switching Assemblies in Structured Neuronal Networks
MT Schaub, YN Billeh, CA Anastassiou, C Koch, M Barahona
PLoS Computational Biology 11 (7), e1004196, 2015
The Ising decoder: reading out the activity of large neural ensembles
MT Schaub, SR Schultz
Journal of computational neuroscience 32 (1), 101-118, 2012
Flow-based network analysis of the Caenorhabditis elegans connectome
KA Bacik, MT Schaub, M Beguerisse-Díaz, YN Billeh, M Barahona
PLoS Comput Biol 12 (8), e1005055, 2016
Revealing cell assemblies at multiple levels of granularity
YN Billeh, MT Schaub, CA Anastassiou, M Barahona, C Koch
Journal of neuroscience methods 236, 92-106, 2014
Different approaches to community detection
M Rosvall, JC Delvenne, MT Schaub, R Lambiotte
Advances in network clustering and blockmodeling, 105-119, 2019
Graph-based semi-supervised & active learning for edge flows
J Jia, MT Schaub, S Segarra, AR Benson
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019
Flow smoothing and denoising: Graph signal processing in the edge-space
MT Schaub, S Segarra
2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP …, 2018
Random multi-hopper model: super-fast random walks on graphs
E Estrada, JC Delvenne, N Hatano, JL Mateos, R Metzler, AP Riascos, ...
Journal of Complex Networks 6 (3), 382-403, 2018
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