Name
Abstract | ||
|---|---|---|
Since the availability of social networks data and the range of these data have significantly grown in recent years, new aspects have to be considered. In this paper we address computational complexity of social networks analysis and clarity of their visualization. Our approach uses combination of Formal Concept Analysis and well-known matrix factorization methods. The goal is to reduce the dimension of social network data and to measure the amount of information which is lost during the reduction. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/WI-IAT.2009.225 | Web Intelligence and Intelligent Agent Technologies, 2009. WI-IAT '09. IEEE/WIC/ACM International Joint Conferences |
Keywords | Field | DocType |
concept lattice,correlation dimension,matrix factorization,two-mode social network | Data mining,Social network,Computer science,Theoretical computer science,Artificial intelligence,Intelligent Network,Data visualization,CLARITY,Visualization,Matrix decomposition,Formal concept analysis,Machine learning,Computational complexity theory | Conference |
Volume | ISBN | Citations |
3 | 978-1-4244-5331-3 | 14 |
PageRank | References | Authors |
0.67 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Václav Snasel | 1 | 1261 | 210.53 |
| Zdenek Horák | 2 | 28 | 3.48 |
| Kocibova, Jana | 3 | 14 | 0.67 |
| Ajith Abraham | 4 | 8954 | 729.23 |