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Abstract
Topological metrics of graphs provide a natural way to describe the prominent features of various types of networks. Graph metrics describe the structure and interplay of graph edges and have found applications in many scientific fields. In this work, graph metrics are used in network estimation by developing optimisation methods that incorporate prior knowledge of a network’s topology. The derivatives of graph metrics are used in gradient descent schemes for weighted undirected network denoising, network completion, and network decomposition. The successful performance of our methodology is shown in a number of toy examples and realworld datasets. Most notably, our work establishes a new link between graph theory, network science and optimisation.
Original language  English 

Pages (fromto)  576586 
Number of pages  11 
Journal  IEEE Transactions on Network Science and Engineering 
Volume  6 
Issue number  3 
Early online date  21 Jun 2018 
DOIs  
Publication status  Published  4 Sep 2019 
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 1 Finished
Datasets

Matlab codes related to "Weighted network estimation by the use of topological graph metrics"
Escudero Rodriguez, J. (Creator) & Spyrou, L. (Creator), Edinburgh DataShare, 29 Jun 2018
DOI: 10.7488/ds/2369, https://datashare.is.ed.ac.uk/handle/10283/3110
Dataset