Carlo V. Cannistraci
According to our database1, Carlo V. Cannistraci authored at least 18 papers between 2010 and 2019.
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Measuring group-separability in geometrical space for evaluation of pattern recognition and embedding algorithms.
Nonlinear Markov Clustering by Minimum Curvilinear Sparse Similarity.
Angular separability of data clusters or network communities in geometrical space and its relevance to hyperbolic embedding.
Navigability evaluation of complex networks by greedy routing efficiency.
Latent Geometry Inspired Graph Dissimilarities Enhance Affinity Propagation Community Detection in Complex Networks.
Minimum curvilinear automata with similarity attachment for network embedding and link prediction in the hyperbolic space.
Pioneering topological methods for network-based drug-target prediction by exploiting a brain-network self-organization theory.
Briefings in Bioinformatics, 2018
Semi-supervised community detection based on non-negative matrix factorization with node popularity.
Inf. Sci., 2017
A nonuniform popularity-similarity optimization (nPSO) model to efficiently generate realistic complex networks with communities.
Local-ring network automata and the impact of hyperbolic geometry in complex network link-prediction.
Rich-clubness test: how to determine whether a complex network has or doesn't have a rich-club?
Can local-community-paradigm and epitopological learning enhance our understanding of how local brain connectivity is able to process, learn and memorize chronic pain?
Applied Network Science, 2017
Machine learning meets network science: dimensionality reduction for fast and efficient embedding of networks in the hyperbolic space.
Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks.
A promoter-level mammalian expression atlas.
Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding.
Poster: Observing change in crowded data sets in 3D space - Visualizing gene expression in human tissues.
Proceedings of the IEEE Symposium on 3D User Interfaces, 2013
Nonlinear dimension reduction and clustering by Minimum Curvilinearity unfold neuropathic pain and tissue embryological classes.