Zobrazeno 1 - 10
of 147
pro vyhledávání: '"Musciotto, A."'
Autor:
Mariani, Manuel S., Battiston, Federico, Horvát, Emőke-Ágnes, Livan, Giacomo, Musciotto, Federico, Wang, Dashun
Publikováno v:
Nature Communications 15 (1), 10701 (2024)
Understanding the collective dynamics behind the success of ideas, products, behaviors, and social actors is critical for decision-making across diverse contexts, including hiring, funding, career choices, and the design of interventions for social c
Externí odkaz:
http://arxiv.org/abs/2412.17472
Teams are the fundamental units propelling innovation and advancing modern science. A rich literature links the fundamental features of teams, such as their size and diversity, to academic success. However, such analyses fail to capture temporal patt
Externí odkaz:
http://arxiv.org/abs/2407.09326
Autor:
Lotito, Quintino Francesco, Contisciani, Martina, De Bacco, Caterina, Di Gaetano, Leonardo, Gallo, Luca, Montresor, Alberto, Musciotto, Federico, Ruggeri, Nicolò, Battiston, Federico
Publikováno v:
Journal of Complex Networks, Volume 11, Issue 3, June 2023
From social to biological systems, many real-world systems are characterized by higher-order, non-dyadic interactions. Such systems are conveniently described by hypergraphs, where hyperedges encode interactions among an arbitrary number of units. He
Externí odkaz:
http://arxiv.org/abs/2303.15356
Publikováno v:
Journal of Complex Networks, Volume 12, Issue 2, April 2024, cnae013
Many networks can be characterised by the presence of communities, which are groups of units that are closely linked. Identifying these communities can be crucial for understanding the system's overall function. Recently, hypergraphs have emerged as
Externí odkaz:
http://arxiv.org/abs/2303.01385
Network dismantling is a relevant research area in network science, gathering attention both from a theoretical and an operational point of view. Here, we propose a general framework for dismantling that prioritizes the removal of nodes that bridge t
Externí odkaz:
http://arxiv.org/abs/2209.14077
We introduce a method for the detection of Statistically Validated Simplices in higher-order networks. Statistically validated simplices represent the maximal sets of nodes of any size that consistently interact collectively and do not include co-int
Externí odkaz:
http://arxiv.org/abs/2209.12712
Network motifs are recurrent, small-scale patterns of interactions observed frequently in a system. They shed light on the interplay between the topology and the dynamics of complex networks across various domains. In this work, we focus on the probl
Externí odkaz:
http://arxiv.org/abs/2209.10241
Autor:
Juri Kivelev, Ilkka Saarenpää, Antti Karlsson, Paride Crisafulli, Federico Musciotto, Jyrki Piilo, Rosario N. Mantegna
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-8 (2024)
Abstract The role of complex network analysis in patients with diagnosis of unruptured intracranial aneurysm is unexplored. The objective of this study is to assess the applicability of this methodology in aneurysm patients. We retrospectively analyz
Externí odkaz:
https://doaj.org/article/50a575e803be40fa8f9214c17216439c
Publikováno v:
Commun Phys 5, 79 (2022)
A deluge of new data on social, technological and biological networked systems suggests that a large number of interactions among system units are not limited to pairs, but rather involve a higher number of nodes. To properly encode such higher-order
Externí odkaz:
http://arxiv.org/abs/2108.03192
Recent empirical evidence has shown that in many real-world systems, successfully represented as networks, interactions are not limited to dyads, but often involve three or more agents at a time. These data are better described by hypergraphs, where
Externí odkaz:
http://arxiv.org/abs/2103.16484