Community detection is one of the major tasks in social networks and can be visualized
as clustering in social networks. In social networks, communities may not be dependent
on a single feature/property. There may be many factors that affect the formation and
behavior of communities. These features may be interdependent on each other. For
example, the communities formed using the feature ‘Tags’ in Blogs may also be
dependent on the property of ‘Authors’ of those blogs, since Authors may use only those
Tags which are related to their own fields. So, a method should be found to determine
which features are related to each other and how closely they are related. Ontology has
been one of tools for semantic web and web-based tools. Such few approaches are
shown by Ganesh et al. (2004), Mukhopadhyay et al. (2007), and Chensheng et al. (2009).
Researchers also applied the concept of ontology to social networks and its analysis
from different perspectives. The authors of this paper like to acknowledge the work of
Wennerberg (2004), Mika (2005), Jung and Euzenat (2007), Peng and Sikun (2009), Chen
et al. (2010), Jamalzadeh and Behravan (2011), Challenger (2012), Sam and Chatwin
(2012), and Lecocq et al. (2013). Further, the work shown in Mishra et al. (2011) became
the basis for the comparison of graph algorithms.
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