This paper examines a schema for graph-theoretic clustering using node-based resilience measures. Node-based resilience measures optimize an objective based on a critical set of nodes whose removal causes some severity of disconnection in the network. Beyond presenting a general framework for the usage of node based resilience measures for variations of clustering problems, we experimentally validate the usefulness of such methods in accomplishing the following: (i) clustering a graph in one step without knowing the number of clusters a priori; (ii) removing noise from noisy data; and (iii) detecting overlapping communities. We demonstrate that this clustering schema can be applied successfully using a wide range of data, including both real and synthetic networks, both natively in graph form and also expressed as point sets
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
complex networks, clustering, data mining, graph theoretic algorithms
Matta, John, Tayo Obafemi-Ajayi, Jeffrey Borwey, Koushik Sinha, Donald Wunsch, and Gunes Ercal. "Node-based resilience measure clustering with applications to noisy and overlapping communities in complex networks." Applied Sciences 8, no. 8 (2018): 1307.