Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/37757
Appears in Collections:Computing Science and Mathematics Book Chapters and Sections
Peer Review Status: Refereed
Title: Graph Pseudometrics from a Topological Point of View
Author(s): Garcia-Pulido, Ana Lucia
Hess, Kathryn
Tan, Jane
Turner, Katharine
Wang, Bei
Yerolemou, Naya
Contact Email: analucia.garciapulido@stir.ac.uk
Editor(s): Gasparovic, Ellen
Robins, Vanessa
Turner, Katharine
Sponsor: Australian Research Council
National Science Foundation
National Science Foundation
Engineering and Physical Sciences Research Council
Engineering and Physical Sciences Research Council
Citation: Garcia-Pulido AL, Hess K, Tan J, Turner K, Wang B & Yerolemou N (2023) Graph Pseudometrics from a Topological Point of View. In: Gasparovic E, Robins V & Turner K (eds.) <i>Research in Computational Topology 2</i>. Association for Women in Mathematics Series. Springer International Publishing, pp. 99-128. https://doi.org/10.1007/978-3-030-95519-9_5
Issue Date: 2023
Date Deposited: 29-Nov-2024
Series/Report no.: Association for Women in Mathematics Series
Abstract: We explore pseudometrics for directed graphs in order to better understand their topological properties. The directed flag complex associated to a directed graph provides a useful bridge between network science and topology. Indeed, it has often been observed that phenomena exhibited by real-world networks reflect the topology of their flag complexes, as measured, for example, by Betti numbers or simplex counts. As it is often computationally expensive (or even unfeasible) to determine such topological features exactly, it would be extremely valuable to have pseudometrics on the set of directed graphs that can both detect the topological differences and be computed efficiently. To facilitate work in this direction, we introduce methods to measure how well a graph pseudometric captures the topology of a directed graph. We then use these methods to evaluate some well-established pseudometrics, using test data drawn from several families of random graphs.
DOI Link: 10.1007/978-3-030-95519-9_5
Licence URL(s): http://www.rioxx.net/licenses/under-embargo-all-rights-reserved

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