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Appl. Sci. 2018, 8(8), 1307; https://doi.org/10.3390/app8081307

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

1
Department of Computer Science, Southern Illinois University Edwardsville, Edwardsville, IL 62025, USA
2
Department of Engineering, Missouri State University, Springfield, MO 65897, USA
3
Google, Inc., Mountain View, CA 94043, USA
4
Department of Computer Science, Southern Illinois University Carbondale, Carbondale, IL 62901, USA
5
Electrical and Computer Engineering Department, Missouri S & T, Rolla, MO 65409, USA
This paper is an extended version of our paper published in ICDM 2016.
*
Author to whom correspondence should be addressed.
Received: 12 July 2018 / Revised: 26 July 2018 / Accepted: 28 July 2018 / Published: 6 August 2018
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Abstract

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. View Full-Text
Keywords: complex networks; clustering; data mining; graph theoretic algorithms complex networks; clustering; data mining; graph theoretic algorithms
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Matta, J.; Obafemi-Ajayi, T.; Borwey, J.; Sinha, K.; Wunsch, D.; Ercal, G. Node-Based Resilience Measure Clustering with Applications to Noisy and Overlapping Communities in Complex Networks. Appl. Sci. 2018, 8, 1307.

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