Community detection is one of the important tasks of social network analysis. It has significant
practical importance for achieving cost-effective solutions for problems in the area of search engine
optimization, spam detection, viral marketing, counter-terrorism, epidemic modeling, etc. In
recent years, there has been an exponential growth of online social platforms such as Twitter, Facebook,
Google+, Pinterest and Tumblr, as people can easily connect to each other in the Internet
era overcoming geographical barriers. This has brought about new forms of social interaction, dialogue,
exchange and collaboration across diverse social networks of unprecedented scales. At the
same time, it presents new challenges and demands more effective, as well as scalable, graphmining
techniques because the extraction of novel and useful knowledge from massive amount of
graph data holds the key to the analysis of social networks in a much larger scale. In this research
paper, the problem to find communities within social networks is considered. Existing community
detection techniques utilize the topological structure of the social network, but a proper combination
of the available attribute data, which represents the properties of the participants or actors,
and the structure data of the social network graph is promising for the detection of more accurate
and meaningful communities.
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