Name
Abstract | ||
|---|---|---|
Many complex networks in the real world demonstrate similar patterns, including the scale-free property and strong community structure. In this paper, we present a novel parameter-free community detection algorithm based on the scale-free property of networks, named ScaleFreeCDA. The basic idea behind it is two mechanisms, i.e., node growth and preferential attachment. Community centers are firstly determined by node degree and similarity, and then community structures are obtained accordingly. The experiments indicate that our algorithm can find high-quality communities, and can determine appropriate number of communities on most artificial and real networks. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/CIT/IUCC/DASC/PICOM.2015.335 | CIT/IUCC/DASC/PICOM 2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION TECHNOLOGY - UBIQUITOUS COMPUTING AND COMMUNICATIONS - DEPENDABLE, AUTONOMIC AND SECURE COMPUTING - PERVASIVE INTELLIGENCE AND COMPUTING |
Keywords | Field | DocType |
Community detection, scale-free property, complex networks, algorithm | Community structure,Computer science,Algorithm,Theoretical computer science,Linear programming,Complex network,Cluster analysis,Preferential attachment | Conference |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiyang Liu | 1 | 159 | 18.55 |
| Qiong Pan | 2 | 0 | 0.34 |
| Yingying An | 3 | 0 | 0.34 |
| Guimin Qin | 4 | 30 | 4.00 |