Rabbany, Reihaneh, Takaffoli, Mansoreh, Fagnan, Justin, Zaïane, Osmar R., and Campello, Ricardo (2017) Relative validity criteria for community mining algorithms. In: Alhajj, Reda, and Rokne, Jon, (eds.) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY, USA, pp. 1-15.

PDF (Published Version) - Published Version Restricted to Repository staff only

Abstract

[Extract] The recent growing trend in the data mining field is the analysis of structured/interrelated data, motivated by the natural presence of relationships between data points in a variety of the present-day applications. The structures in these interrelated data are usually represented using networks, known as complex networks or information networks; examples are the hyperlink networks of web pages, citation or collaboration networks of scholars, biological networks of genes or proteins, trust and social networks of humans, and much more. All these networks exhibit common statistical properties, such as power law degree distribution, small-world phenomenon, relatively high transitivity, shrinking diameter, and densification power laws (Newman 2010; Leskovec et al. 2005). Network clustering, a.k.a. community mining, is one of the principal tasks in the analysis of complex networks. Many community mining algorithms have been proposed in recent years (for a survey, refer to Fortunato 2010). These algorithms evolved very quickly from simple heuristic approaches to more sophisticated optimization-based methods that are explicitly or implicitly trying to maximize the goodness of the discovered communities. The broadly used explicit maximization objective is the modularity, first introduced by Newman and Girvan (2004).

Item ID:	49456
Item Type:	Book Chapter (Later Edition)
ISBN:	978-1-4614-7163-9
Related URLs:	https://researchonline.jcu.edu.au/47683/
Additional Information:	The record for the earlier edition can be found at the link in the related URL field.
Date Deposited:	09 Jan 2018 01:54
FoR Codes:	01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 100%
SEO Codes:	97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
Downloads:	Total: 5
	More Statistics