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pro vyhledávání: '"Peixoto, Tiago"'
Autor:
Peixoto, Tiago P.
A fundamental problem associated with the task of network reconstruction from dynamical or behavioral data consists in determining the most appropriate model complexity in a manner that prevents overfitting, and produces an inferred network with a st
Externí odkaz:
http://arxiv.org/abs/2405.01015
Autor:
Peixoto, Tiago P.
Network reconstruction consists in determining the unobserved pairwise couplings between $N$ nodes given only observational data on the resulting behavior that is conditioned on those couplings -- typically a time-series or independent samples from a
Externí odkaz:
http://arxiv.org/abs/2401.01404
Social networks have been widely studied over the last century from multiple disciplines to understand societal issues such as inequality in employment rates, managerial performance, and epidemic spread. Today, these and many more issues can be studi
Externí odkaz:
http://arxiv.org/abs/2306.11004
Autor:
Peixoto, Tiago P., Kirkley, Alec
Publikováno v:
Phys. Rev. E 108, 024309 (2023)
The task of community detection, which aims to partition a network into clusters of nodes to summarize its large-scale structure, has spawned the development of many competing algorithms with varying objectives. Some community detection methods are i
Externí odkaz:
http://arxiv.org/abs/2210.09186
Autor:
Peixoto, Tiago P.
Publikováno v:
Phys. Rev. E 106, 024305 (2022)
We develop a method to infer community structure in directed networks where the groups are ordered in a latent one-dimensional hierarchy that determines the preferred edge direction. Our nonparametric Bayesian approach is based on a modification of t
Externí odkaz:
http://arxiv.org/abs/2203.16460
We perform a systematic analysis of the quality of fit of the stochastic block model (SBM) for 275 empirical networks spanning a wide range of domains and orders of size magnitude. We employ posterior predictive model checking as a criterion to asses
Externí odkaz:
http://arxiv.org/abs/2201.01658
Autor:
Peixoto, Tiago P.
Publikováno v:
Elements in the Structure and Dynamics of Complex Networks, Cambridge University Press (2023)
Community detection is one of the most important methodological fields of network science, and one which has attracted a significant amount of attention over the past decades. This area deals with the automated division of a network into fundamental
Externí odkaz:
http://arxiv.org/abs/2112.00183
Autor:
Battiston, Federico, Amico, Enrico, Barrat, Alain, Bianconi, Ginestra, de Arruda, Guilherme Ferraz, Franceschiello, Benedetta, Iacopini, Iacopo, Kéfi, Sonia, Latora, Vito, Moreno, Yamir, Murray, Micah M., Peixoto, Tiago P., Vaccarino, Francesco, Petri, Giovanni
Publikováno v:
Nature Physics 17, 1093-1098 (2021)
Complex networks have become the main paradigm for modelling the dynamics of interacting systems. However, networks are intrinsically limited to describing pairwise interactions, whereas real-world systems are often characterized by higher-order inte
Externí odkaz:
http://arxiv.org/abs/2110.06023
Autor:
Hyland, Charles C., Tao, Yuanming, Azizi, Lamiae, Gerlach, Martin, Peixoto, Tiago P., Altmann, Eduardo G.
Publikováno v:
EPJ Data Science volume 10, Article number: 33 (2021)
We are interested in the widespread problem of clustering documents and finding topics in large collections of written documents in the presence of metadata and hyperlinks. To tackle the challenge of accounting for these different types of datasets,
Externí odkaz:
http://arxiv.org/abs/2106.15821