193 citations · 487 across the 12 of their papers we have counts for
12 papers
Geometrical congruence and efficient greedy navigability of complex networks
Carlo Vittorio Cannistraci, Alessandro Muscoloni
Hyperbolic networks are supposed to be congruent with their underlying latent geometry and following geodesics in the hyperbolic space is believed equivalent to navigate through to…
Modular gateway-ness connectivity and structural core organization in maritime network science
Mengqiao Xu, Qian Pan, Alessandro Muscoloni +2
Around 80% of global trade by volume is transported by sea, and thus the maritime transportation system is fundamental to the world economy. To better exploit new international shi…
Nonlinear Markov Clustering by Minimum Curvilinear Sparse Similarity
C. Duran, A. Acevedo, S. Ciucci +2
The development of algorithms for unsupervised pattern recognition by nonlinear clustering is a notable problem in data science. Markov clustering (MCL) is a renowned algorithm tha…
Angular separability of data clusters or network communities in geometrical space and its relevance to hyperbolic embedding
Alessandro Muscoloni, Carlo Vittorio Cannistraci
Analysis of 'big data' characterized by high-dimensionality such as word vectors and complex networks requires often their representation in a geometrical space by embedding. Recen…
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…
Latent Geometry Inspired Graph Dissimilarities Enhance Affinity Propagation Community Detection in Complex Networks
Carlo Vittorio Cannistraci, Alessandro Muscoloni
Affinity propagation is one of the most effective unsupervised pattern recognition algorithms for data clustering in high-dimensional feature space. However, the numerous attempts…
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,…
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…
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…
Coalescent embedding in the hyperbolic space unsupervisedly discloses the hidden geometry of the brain
Alberto Cacciola, Alessandro Muscoloni, Vaibhav Narula +9
The human brain displays a complex network topology, whose structural organization is widely studied using diffusion tensor imaging. The original geometry from which emerges the ne…
Rich-clubness test: how to determine whether a complex network has or doesn't have a rich-club?
Alessandro Muscoloni, Carlo Vittorio Cannistraci
The rich-club concept has been introduced in order to characterize the presence of a cohort of nodes with a large number of links (rich nodes) that tend to be well connected betwee…
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…