Detecting Commmunities via Simultaneous Clustering of Graphs and Folksonomies

We present a simple technique for detecting communities by utilizing both the link structure and folksonomy (or tag) information that is readily available in most social media systems. A simple way to describe our approach is by defining a community as a set of nodes in a graph that link more frequently to within this set than outside it and they share similar tags. Our technique is based on the Normalized Cut (NCut) algorithm and can be easily and efficiently implemented. We validate our method by using a real network of blogs and tag information obtained from a social bookmarking site. We also verify our results on a citation network for which we have access to ground truth cluster information. Our method, Simultaneous Cut (SimCut), has the advantage that it can group related tags and cluster the nodes simultaneously.
Date: August 24, 2008
Book Title: Proceedings of the Tenth Workshop on Web Mining and Web Usage Analysis (WebKDD)
Type: InProceedings
Publisher: ACM
Note: (Held in conjunction with The 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2008))
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Bibtex


@InProceedings{Detecting_Commmunities_via_Simultaneous_,
  author = "Akshay Java and Anupam Joshi and Tim Finin",
  title = "{Detecting Commmunities via Simultaneous Clustering of Graphs and Folksonomies}",
  month = "August",
  year = "2008",
  note = "(Held in conjunction with The 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2008))",
  booktitle = "Proceedings of the Tenth Workshop on Web Mining and Web Usage Analysis (WebKDD)",
  publisher = "ACM ",
}