2017/05/31 by Loukianos Spyrou, Javier Escudero
Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Complex Network Analysis Techniques #Geometric networks #Gradient descent #Graph #Graph Theory and Algorithms #Graph property #Network science #Null graph #Random geometric graph #Topological graph theory #Weighted network #cs.DS #cs.SI #msc:05C22 #msc:62H12
paper · pdf · doi:10.1109/tnse.2018.2849342
Accepted for publication in IEEE Transactions on Network Science and Engineering
openalex created_date 2017/05/12 · arxiv created 2018/06/19 · arxiv updated 2018/06/21 · openalex publication_date 2018/06/21 · openalex updated_date 2026/08/05
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 real-world datasets. Most notably, our work establishes a new link between graph theory, network science and optimisation.