TY - GEN
T1 - Density-based community identification and visualisation
AU - Kozielski, Michal
AU - Filipowski, Wojciech
AU - Popowicz, Dominik
AU - Warchal, Łukasz
PY - 2012
Y1 - 2012
N2 - Community can be generally defined as a subgraph where nodes are more densely connected with each other than with the rest of a network. Such definition makes application of density-based clustering methods to community identification justified and natural. Moreover, density-based methods have many extensions enabling their application to complex data analysis. Therefore, the analysis of the characteristics of density-based clustering methods in application to community identification is important and valuable. The article presents and evaluates new similarity measures that can be utilised by the approaches to density-based community identification. Several experiments on real life and generated networks are performed to show and explain the differences between these measures and to compare them with other methods. The results show that the new measures improve the quality of analysis and that density-based clustering algorithms can be valuable community identification methods.
AB - Community can be generally defined as a subgraph where nodes are more densely connected with each other than with the rest of a network. Such definition makes application of density-based clustering methods to community identification justified and natural. Moreover, density-based methods have many extensions enabling their application to complex data analysis. Therefore, the analysis of the characteristics of density-based clustering methods in application to community identification is important and valuable. The article presents and evaluates new similarity measures that can be utilised by the approaches to density-based community identification. Several experiments on real life and generated networks are performed to show and explain the differences between these measures and to compare them with other methods. The results show that the new measures improve the quality of analysis and that density-based clustering algorithms can be valuable community identification methods.
UR - https://www.scopus.com/pages/publications/84874257718
U2 - 10.1109/ASONAM.2012.198
DO - 10.1109/ASONAM.2012.198
M3 - Conference contribution
AN - SCOPUS:84874257718
SN - 9780769547992
T3 - Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012
SP - 1149
EP - 1152
BT - Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012
PB - IEEE Computer Society
T2 - 4th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012
Y2 - 26 August 2012 through 29 August 2012
ER -