Name
Abstract | ||
|---|---|---|
The analysis of social networks is concentrated especially on uncovering hidden relations and properties of network members (vertices). Most of the current approaches are focused mainly on different network types and different network coefficients. On one hand, the analysis can be relatively simple, on the other hand some complex approaches to network dynamics can be used. This paper introduces a novel aspect of network analysis based on the so-called Forgetting Curve. For network vertices and edges, we define two coefficients, which describe their role in the network depending on their long-term behavior. Using one of these parameters we reduce the network to smaller components. We provide some experimental results using DBLP dataset. Our research illustrates the usefulness of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/CASoN.2010.120 | Computational Aspects of Social Networks |
Keywords | Field | DocType |
social network,social network reduction,network member,network analysis,complex approach,different network type,current approach,network dynamic,different network coefficient,dblp dataset,network vertex,network dynamics,data visualization,memory,stability,history,stability analysis,complexity reduction,visualization | Network science,Network formation,Dynamic network analysis,Data mining,Network dynamics,Social network,Computer science,Network simulation,Forgetting curve,Artificial intelligence,Network analysis,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4244-8785-1 | 18 | 1.05 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Milos Kudelka | 1 | 116 | 23.81 |
| Zdenek Horak | 2 | 87 | 12.96 |
| Václav Snasel | 3 | 1261 | 210.53 |
| Ajith Abraham | 4 | 8954 | 729.23 |