A particle-and-density based evolutionary clustering method for dynamic networks
Proceedings of the VLDB Endowment
| Hi-index | 0.00 |
Many systems in sciences, engineering and nature can be modeled as networks. Examples are internet, metabolic networks and social networks. Network clustering algorithms aimed to find hidden structures from networks are important to make sense of complex networked data. In this paper we present a new clustering method for networks. The proposed algorithm can find hierarchical structure of clusters without requiring any input parameters. The experiments using real data demonstrate an outstanding performance of the new method.