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Ester, M., Kriegel, H.-P., Sander, J. and Xu, X. (1996) A Density-Based Algorithm for Discovering Communities in Large Spatial Databases with Noise. Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96).

has been cited by the following article:

• TITLE: Community Detection in Dynamic Social Networks

  AUTHORS: Nathan Aston, Wei Hu

  KEYWORDS: Community Detection, Dynamic Social Networks, Density, Genetic Algorithms

  JOURNAL NAME: Communications and Network, Vol.6 No.2, May 28, 2014

  ABSTRACT: There are many community detection algorithms for discovering communities in networks, but very few deal with networks that change structure. The SCAN (Structural Clustering Algorithm for Networks) algorithm is one of these algorithms that detect communities in static networks. To make SCAN more effective for the dynamic social networks that are continually changing their structure, we propose the algorithm DSCAN (Dynamic SCAN) which improves SCAN to allow it to update a local structure in less time than it would to run SCAN on the entire network. We also improve SCAN by removing the need for parameter tuning. DSCAN, tested on real world dynamic networks, performs faster and comparably to SCAN from one timestamp to another, relative to the size of the change. We also devised an approach to genetic algorithms for detecting communities in dynamic social networks, which performs well in speed and modularity.