Node-Based Resilience Measure Clustering with Applications to Noisy and Overlapping Communities in Complex Networks
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
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.