activity
20172020
most citedNavigability evaluation of complex networks by greedy routing efficiency

35 citations · 64 across the 5 of their papers we have counts for

collaborators

8 papers

cs.SI20201 cited

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…

physics.soc-ph2020

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…

cs.LG20192 cited

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…

cs.LG20193 cited

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…

physics.soc-ph201935 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…

cs.LG2018

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…