Zobrazeno 1 - 10
of 1 590
pro vyhledávání: '"Garlaschelli, A."'
Lying at the interface between Network Science and Machine Learning, node embedding algorithms take a graph as input and encode its structure onto output vectors that represent nodes in an abstract geometric space, enabling various vector-based downs
Externí odkaz:
http://arxiv.org/abs/2412.04354
Autor:
Macchiati, Valentina, Marchese, Emiliano, Mazzarisi, Piero, Garlaschelli, Diego, Squartini, Tiziano
The level of systemic risk in economic and financial systems is strongly determined by the structure of the underlying networks of interdependent entities that can propagate shocks and stresses. Since changes in network structure imply changes in ris
Externí odkaz:
http://arxiv.org/abs/2409.03349
Autor:
Fessina, Massimiliano, Cimini, Giulio, Squartini, Tiziano, Astudillo-Estévez, Pablo, Thurner, Stefan, Garlaschelli, Diego
Production networks constitute the backbone of every economic system. They are inherently fragile as several recent crises clearly highlighted. Estimating the system-wide consequences of local disruptions (systemic risk) requires detailed information
Externí odkaz:
http://arxiv.org/abs/2408.02467
Most of the analyses concerning signed networks have focused on the balance theory, hence identifying frustration with undirected, triadic motifs having an odd number of negative edges; much less attention has been paid to their directed counterparts
Externí odkaz:
http://arxiv.org/abs/2407.08697
According to the so-called strong version of structural balance theory, actors in signed social networks avoid establishing triads with an odd number of negative links. Generalising, the weak version of balance theory allows for nodes to be partition
Externí odkaz:
http://arxiv.org/abs/2404.15914
Autor:
Lalli, Margherita, Garlaschelli, Diego
Recent research has tried to extend the concept of renormalization, which is naturally defined for geometric objects, to more general networks with arbitrary topology. The current attempts do not naturally apply to directed networks, for instance bec
Externí odkaz:
http://arxiv.org/abs/2403.00235
Publikováno v:
Chaos Solitons & Fractals 186 (2024)
Networks of financial exposures are the key propagators of risk and distress among banks, but their empirical structure is not publicly available because of confidentiality. This limitation has triggered the development of methods of network reconstr
Externí odkaz:
http://arxiv.org/abs/2402.11136
Temporal Networks, and more specifically, Markovian Temporal Networks, present a unique challenge regarding the community discovery task. The inherent dynamism of these systems requires an intricate understanding of memory effects and structural hete
Externí odkaz:
http://arxiv.org/abs/2402.10141
One of the main goals of developmental biology is to reveal the gene regulatory networks (GRNs) underlying the robust differentiation of multipotent progenitors into precisely specified cell types. Most existing methods to infer GRNs from experimenta
Externí odkaz:
http://arxiv.org/abs/2401.07379
We analyse the largest eigenvalue of the adjacency matrix of the configuration model with large degrees, where the latter are treated as hard constraints. In particular, we compute the expectation of the largest eigenvalue for degrees that diverge as
Externí odkaz:
http://arxiv.org/abs/2312.07812