Strong connectivity and its applications
Peteris Daugulis
TL;DR
This paper explores the properties of strong connectivity invariants in directed graphs, focusing on their computation and applications in neuroscience connectome graphs.
Contribution
It reviews properties of strong vertex and edge connectivities and presents computational results on neuroscience connectome graphs.
Findings
Properties of strong connectivity invariants analyzed
Computational results for neuroscience connectome graphs presented
Insights into graph robustness and vulnerability
Abstract
Directed graphs are widely used in modelling of nonsymmetric relations in various sciences and engineering disciplines. We discuss invariants of strongly connected directed graphs - minimal number of vertices or edges necessary to remove to make remaining graphs not strongly connected. By analogy with undirected graphs these invariants are called strong vertex/edge connectivities. We review some properties of these invariants. Computational results for some publicly available connectome graphs used in neuroscience are described.
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Taxonomy
TopicsComplex Network Analysis Techniques · Topological and Geometric Data Analysis · Graph theory and applications