DENGRAPH A Density-based Community Detection Algorithm

• Published in: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence pp. 112 - 115
• Main Authors: Falkowski, Tanja, Barth, Anja, Spiliopoulou, Myra
• Format: Conference Proceeding
• Language: English
• Published: Washington, DC, USA IEEE Computer Society 02.11.2007
• Series: ACM Conferences
• Subjects: Computing methodologies > Machine learning > Learning paradigms > Unsupervised learning > Cluster analysis; Human-centered computing > Collaborative and social computing; Information systems > Information retrieval > Evaluation of retrieval results; Information systems > Information storage systems > Storage architectures > Storage network architectures; Mathematics of computing > Discrete mathematics > Graph theory > Graph algorithms
• ISBN: 0769530265; 9780769530260
• DOI: 10.1109/WI.2007.43

Detecting densely connected subgroups in graphs such as communities in social networks is of interest in many research fields. Several methods have been developed to find communities but most of them have a high time complexity and are thus not applicable for large networks. Inspired by the clustering algorithm incremental DBSCAN we propose a density-based graph clustering algorithm DENGRAPH that is designed to deal with large dynamic datasets with noise and present first experimental results.