Zobrazeno 1 - 10
of 100
pro vyhledávání: '"Tiago P. Peixoto"'
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-15 (2022)
Theoretical models and structures recovered from measured data serve for analysis of complex networks. The authors discuss here existing gaps between theoretical methods and real-world applied networks, and potential ways to improve the interplay bet
Externí odkaz:
https://doaj.org/article/9f36135d37d44abf95778ae28110b28c
Publikováno v:
Communications Physics, Vol 5, Iss 1, Pp 1-7 (2022)
Externí odkaz:
https://doaj.org/article/fc4fca1c79ac4e3c9b3d16b4975637b3
Autor:
Charles C. Hyland, Yuanming Tao, Lamiae Azizi, Martin Gerlach, Tiago P. Peixoto, Eduardo G. Altmann
Publikováno v:
EPJ Data Science, Vol 10, Iss 1, Pp 1-16 (2021)
Abstract 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 d
Externí odkaz:
https://doaj.org/article/4b9e5123e3de42f9a6295b26d8443f1d
Publikováno v:
Communications Physics, Vol 4, Iss 1, Pp 1-11 (2021)
Higher-order interactions intervene in a large variety of networked phenomena, from shared interests known to influence the creation of social ties, to co-location shaping networks embedded in space, like power grids. This work introduces a Bayesian
Externí odkaz:
https://doaj.org/article/c1160a8ebacd4688866a4ca02fb478c4
Autor:
Tiago P. Peixoto
Publikováno v:
Physical Review X, Vol 12, Iss 1, p 011004 (2022)
Network homophily, the tendency of similar nodes to be connected, and transitivity, the tendency of two nodes to be connected if they share a common neighbor, are conflated properties in network analysis since one mechanism can drive the other. Here,
Externí odkaz:
https://doaj.org/article/1d66bc4a10bc4be58f3164c0b748817b
Autor:
Tiago P. Peixoto, Martin Rosvall
Publikováno v:
Nature Communications, Vol 8, Iss 1, Pp 1-12 (2017)
The description of temporal networks is usually simplified in terms of their dynamic community structures, whose identification however relies on a priori assumptions. Here the authors present a data-driven method that determines relevant timescales
Externí odkaz:
https://doaj.org/article/9c4ca19f703645a78bf2ba4f821df8c7
Autor:
Tiago P. Peixoto
Publikováno v:
Physical Review X, Vol 11, Iss 2, p 021003 (2021)
Community detection methods attempt to divide a network into groups of nodes that share similar properties, thus revealing its large-scale structure. A major challenge when employing such methods is that they are often degenerate, typically yielding
Externí odkaz:
https://doaj.org/article/79f3b4f4847d408eb286f3bdd273ccd4
Autor:
Lizhi Zhang, Tiago P. Peixoto
Publikováno v:
Physical Review Research, Vol 2, Iss 4, p 043271 (2020)
We develop a principled methodology to infer assortative communities in networks based on a nonparametric Bayesian formulation of the planted partition model. We show that this approach succeeds in finding statistically significant assortative module
Externí odkaz:
https://doaj.org/article/45da26723cb24497af23b5b91aba02b5
Autor:
Tiago P. Peixoto
Publikováno v:
Physical Review X, Vol 8, Iss 4, p 041011 (2018)
The vast majority of network data sets contain errors and omissions, although this fact is rarely incorporated in traditional network analysis. Recently, an increasing effort has been made to fill this methodological gap by developing network-reconst
Externí odkaz:
https://doaj.org/article/5aa6571fa0a34b1eb53c3648e5f66429
Autor:
Guilherme Ferraz de Arruda, Emanuele Cozzo, Tiago P. Peixoto, Francisco A. Rodrigues, Yamir Moreno
Publikováno v:
Physical Review X, Vol 7, Iss 1, p 011014 (2017)
We present a continuous formulation of epidemic spreading on multilayer networks using a tensorial representation, extending the models of monoplex networks to this context. We derive analytical expressions for the epidemic threshold of the susceptib
Externí odkaz:
https://doaj.org/article/b11fe1117b894a2aa4da4432f9a10e4f