A Comparison of Community Detection Algorithms on Artificial Networks
DS '09 Proceedings of the 12th International Conference on Discovery Science
Community detection in Social Media
Data Mining and Knowledge Discovery
A density-based approach for mining overlapping communities from social network interactions
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
Density-based Community Identification and Visualisation
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
OCTracker: A Density-Based Framework for Tracking the Evolution of Overlapping Communities in OSNs
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Three-objective subgraph mining using multiobjective evolutionary programming
Journal of Computer and System Sciences
| Hi-index | 0.00 |
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.