Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

You searched for id:"oai:qucosa:de:qucosa:34911". One record found.

Search Limiters

Last 2 Years | English Only

No search limiters apply to these results.

▼ Search Limiters

1. Muscoloni, Alessandro. Generative modelling and inverse problem solving for networks in hyperbolic space.

Degree: PhD, Fakultät Informatik, 2019, Technische Universität Dresden

The investigation of the latent geometrical 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 associated to the structural organization of many real complex systems. The popularity-similarity-optimization (PSO) generative model is able to grow random geometric graphs in the hyperbolic space with realistic properties such as clustering, small-worldness, scale-freeness and rich-clubness. However, it misses to reproduce an important feature of real complex systems, which is the community organization. Here, we introduce the nonuniform PSO (nPSO) generative model, a generalization of the PSO model with a tailored community structure, and we provide an efficient algorithmic implementation with a O(EN) time complexity, where N is the number of nodes and E the number of edges. Meanwhile, in recent years, the inverse problem has also gained increasing attention: given a network topology, how to provide an accurate mapping into its latent geometrical space. Unlike previous attempts based on a computationally expensive maximum likelihood optimization (whose time complexity is between O(N3) and O(N4)), here we show that a class of methods based on nonlinear dimensionality reduction can solve the problem with higher precision and reducing the time complexity to O(N2). Advisors/Committee Members: Cannistraci, Carlo Vittorio (advisor), Schroeder, Michael (referee), Mangioni, Giuseppe (referee).

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Muscoloni, A. (2019). Generative modelling and inverse problem solving for networks in hyperbolic space. (Doctoral Dissertation). Technische Universität Dresden. Retrieved from http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa2-349110; 167138170X

Chicago Manual of Style (16th Edition):

Muscoloni, Alessandro. “Generative modelling and inverse problem solving for networks in hyperbolic space.” 2019. Doctoral Dissertation, Technische Universität Dresden. Accessed August 24, 2019. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa2-349110; 167138170X.

MLA Handbook (7th Edition):

Muscoloni, Alessandro. “Generative modelling and inverse problem solving for networks in hyperbolic space.” 2019. Web. 24 Aug 2019.

Vancouver:

Muscoloni A. Generative modelling and inverse problem solving for networks in hyperbolic space. [Internet] [Doctoral dissertation]. Technische Universität Dresden; 2019. [cited 2019 Aug 24]. Available from: http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa2-349110; 167138170X.

Council of Science Editors:

Muscoloni A. Generative modelling and inverse problem solving for networks in hyperbolic space. [Doctoral Dissertation]. Technische Universität Dresden; 2019. Available from: http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa2-349110; 167138170X

.