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Complexity
Volume 2019, Article ID 1439415, 14 pages
https://doi.org/10.1155/2019/1439415
Research Article

NOESIS: A Framework for Complex Network Data Analysis

Department of Computer Science and Artificial Intelligence & Research Center for Information and Communications Technologies (CITIC), University of Granada, Granada, Spain

Correspondence should be addressed to Fernando Berzal; gro.mca@lazreb

Received 28 June 2019; Accepted 9 September 2019; Published 31 October 2019

Academic Editor: Giulio Cimini

Copyright © 2019 Víctor Martínez et al. 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.

Abstract

Network data mining has attracted a lot of attention since a large number of real-world problems have to deal with complex network data. In this paper, we present NOESIS, an open-source framework for network-based data mining. NOESIS features a large number of techniques and methods for the analysis of structural network properties, network visualization, community detection, link scoring, and link prediction. The proposed framework has been designed following solid design principles and exploits parallel computing using structured parallel programming. NOESIS also provides a stand-alone graphical user interface allowing the use of advanced software analysis techniques to users without prior programming experience. This framework is available under a BSD open-source software license.