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/ASONAM.2009.48 | ASONAM |
Keywords | Field | DocType |
well-known matrix factorization method,social networks analysis,social networks data,matrix factorization methods,formal concept analysis,social network data,new aspect,social network,recent year,computational complexity,correlation dimension,data analysis,social network analysis,matrix decomposition,matrix factorization | Data mining,CLARITY,Social network,Visualization,Computer science,Matrix decomposition,Social network analysis,Theoretical computer science,Correlation dimension,Formal concept analysis,Computational complexity theory | Conference |
Citations | PageRank | References |
2 | 0.38 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Václav Snáel | 1 | 37 | 10.63 |
| Zdenek Horák | 2 | 28 | 3.48 |
| Jana Kocíbová | 3 | 2 | 0.38 |
| Ajith Abraham | 4 | 8954 | 729.23 |