A Systematic Approach for Studying How Topological Measurements Respond to Complex Networks Modifications
Alexandre Benatti, Roberto M. Cesar Jr., Luciano da F. Costa

TL;DR
This paper systematically investigates how various topological measurements of complex networks respond to modifications like edge removal or rewiring, using similarity indices and hierarchical clustering to analyze different network types.
Contribution
It introduces a comprehensive framework combining similarity measures and hierarchical clustering to quantify and visualize the impact of network modifications on topological measurements.
Findings
Erdős-Rényi and Barabási-Albert networks respond similarly to modifications.
Geographical networks exhibit more heterogeneous topological features.
Three types of topological changes are identified as a result of network modifications.
Abstract
Different types of graphs and complex networks have been characterized, analyzed, and modeled based on measurements of their respective topology. However, the available networks may constitute approximations of the original structure as a consequence of sampling incompleteness, noise, and/or error in the representation of that structure. Therefore, it becomes of particular interest to quantify how successive modifications may impact a set of adopted topological measurements, and how respectively undergone changes can be interrelated, which has been addressed in this paper by considering similarity networks and hierarchical clustering approaches. These studies are developed respectively to several topological measurements (accessibility, degree, hierarchical degree, clustering coefficient, betweenness centrality, assortativity, and average shortest path) calculated from complex networks…
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Taxonomy
TopicsComplex Network Analysis Techniques · Advanced Graph Neural Networks · Graph Theory and Algorithms
