Next Article in Journal
Entropy Diversity in Multi-Objective Particle Swarm Optimization
Next Article in Special Issue
Global Inequality in Energy Consumption from 1980 to 2010
Previous Article in Journal
Consistency and Generalization Bounds for Maximum Entropy Density Estimation
Previous Article in Special Issue
Generalized (c,d)-Entropy and Aging Random Walks
Open AccessArticle

Structural Patterns in Complex Systems Using Multidendrograms

1
Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Av. Països Catalans 26, Tarragona 43007, Spain
2
Departament d'Enginyeria Química, Universitat Rovira i Virgili, Av. Països Catalans 26, Tarragona 43007, Spain
*
Author to whom correspondence should be addressed.
Entropy 2013, 15(12), 5464-5474; https://doi.org/10.3390/e15125464
Received: 9 October 2013 / Revised: 22 November 2013 / Accepted: 26 November 2013 / Published: 9 December 2013
(This article belongs to the Special Issue Complex Systems)
Complex systems are usually represented as an intricate set of relations between their components forming a complex graph or network. The understanding of their functioning and emergent properties are strongly related to their structural properties. The finding of structural patterns is of utmost importance to reduce the problem of understanding the structure–function relationships. Here we propose the analysis of similarity measures between nodes using hierarchical clustering methods. The discrete nature of the networks usually leads to a small set of different similarity values, making standard hierarchical clustering algorithms ambiguous. We propose the use of multidendrograms, an algorithm that computes agglomerative hierarchical clusterings implementing a variable-group technique that solves the non-uniqueness problem found in the standard pair-group algorithm. This problem arises when there are more than two clusters separated by the same maximum similarity (or minimum distance) during the agglomerative process. Forcing binary trees in this case means breaking ties in some way, thus giving rise to different output clusterings depending on the criterion used. Multidendrograms solves this problem by grouping more than two clusters at the same time when ties occur. View Full-Text
Keywords: patterns in networks; hierarchical clustering; dendrogram; uniqueness patterns in networks; hierarchical clustering; dendrogram; uniqueness
Show Figures

Figure 1

MDPI and ACS Style

Gómez, S.; Fernández, A.; Granell, C.; Arenas, A. Structural Patterns in Complex Systems Using Multidendrograms. Entropy 2013, 15, 5464-5474.

Show more citation formats Show less citations formats

Article Access Map

1
Only visits after 24 November 2015 are recorded.
Back to TopTop