Community Analysis

In 2007, Falkowski et al. [1] presented DenGraph - a density based graph clustering algorithm for community detection. The algorithm overcomes the complexity problem, and therefore is able to handle also huge networks. Later on, the authors presented in [2] and [3] the even more efficient incremental version DenGraph-IO that adapts an existing clustering when the underlaying graph structure changes. In 2008, DenGraph was used for a temporal analysis of the Enron email data set. In 2009, we applied the proposed method on a music data set to analyse the music listen behaviour of users on the platform. Based on the incremental algorithm we present the evolution of groups of users with similar music listening behavior over time in [4] and [5].

The experiments and the results of this dDM study are published as a part of Falkowski's PhD theses that is also available as book Falkowski[6].


  1. Falkowski T, Barth A, Spiliopoulou M. DENGRAPH: A Density-based Community Detection Algorithm. In: In Proc. of the 2007 IEEE / WIC / ACM International Conference on.; 2007. p. 112-5.
  2. Falkowski T, Barth A, Spiliopoulou M. Studying Community Dynamics with an Incremental Graph Mining Algorithm. In: Proc. of the 14 th Americas Conference on Information Systems (AMCIS).; 2008.
  3. Falkowski T, Barth A. Density-based Temporal Graph Clustering for Subgroup Detection in Social Networks.; 2007.
  4. Falkowski T, Schlitter N. Analyzing the Music Listening Behavior and its Temporal Dynamics Using Data from a Social Networking Site. Zurich; 2008.
  5. Schlitter N, Falkowski T. Mining the Dynamics of Music Preferences from a Social Networking Site. In: Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining. Athens: IEEE Computer Society; 2009. p. 243-8.
  6. Falkowski T. Community Analysis in Dynamic Social Networks. Goettingen: Sierke Verlag; 2009.