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20112020
most citedMachine learning meets network science: dimensionality reduction for fast and efficient embedding of networks in the hyperbolic space

193 citations

9 papers

q-bio.CB2020

Transcriptional landscape of SARS-CoV-2 infection dismantles pathogenic pathways activated by the virus, proposes unique sex-specific differences and predicts tailored therapeutic strategies

Paolo Fagone, Rosella Ciurleo, Salvo Danilo Lombardo +6

The emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease (COVID-19) has posed a serious threat to global health. As no specific therapeutics are ye…

physics.soc-ph2019★ 35 cited

Navigability evaluation of complex networks by greedy routing efficiency

Alessandro Muscoloni, Carlo Vittorio Cannistraci

Network navigability is a key feature of complex networked systems. For a network embedded in a geometrical space, maximization of greedy routing (GR) measures based on the node ge…

physics.soc-ph2018★ 14 cited

Minimum curvilinear automata with similarity attachment for network embedding and link prediction in the hyperbolic space

Alessandro Muscoloni, Carlo Vittorio Cannistraci

The idea of minimum curvilinearity (MC) is that the hidden geometry of complex networks, in particular when they are sufficiently sparse, clustered, small-world and heterogeneous,…

physics.soc-ph2017★ 96 cited

A nonuniform popularity-similarity optimization (nPSO) model to efficiently generate realistic complex networks with communities

Alessandro Muscoloni, Carlo Vittorio Cannistraci

The hidden metric space behind complex network topologies is a fervid topic in current network science and the hyperbolic space is one of the most studied, because it seems associa…

physics.soc-ph2017★ 14 cited

Local-ring network automata and the impact of hyperbolic geometry in complex network link-prediction

Alessandro Muscoloni, Umberto Michieli, Carlo Vittorio Cannistraci

Topological link-prediction can exploit the entire network topology (global methods) or only the neighbourhood (local methods) of the link to predict. Global methods are believed t…

cond-mat.dis-nn2016★ 193 cited

Machine learning meets network science: dimensionality reduction for fast and efficient embedding of networks in the hyperbolic space

Josephine Maria Thomas, Alessandro Muscoloni, Sara Ciucci +2

Complex network topologies and hyperbolic geometry seem specularly connected, and one of the most fascinating and challenging problems of recent complex network theory is to map a…

astro-ph.CO2012★ 81 cited

Moderately Luminous type II Supernovae

C. Inserra, A. Pastorello, M. Turatto +10

Core-collapse Supernovae (CC-SNe) descend from progenitors more massive than about 8 Msun. Because of the young age of the progenitors, the ejecta may eventually interact with the…

astro-ph.SR2011

Calibrating hydrogen-rich core-collapse supernovae for their use as distance indicators independently of type Ia supernovae

M. L. Pumo, L. Zampieri

Using our new general-relativistic, radiation hydrodynamics, Lagrangian code, we computed a rather extended grid of hydrogen-rich core-collapse supernova (CC-SN) models and explore…

astro-ph.SR2011★ 64 cited

Radiation-hydrodynamical modelling of Core-Collapse Supernovae: light curves and the evolution of photospheric velocity and temperature

M. L. Pumo, L. Zampieri

We have developed a relativistic, radiation-hydrodynamics Lagrangian code, specifically tailored to simulate the evolution of the main observables (light curve, evolution of photos…