Name
Abstract | ||
|---|---|---|
Many real world data or process eave a network structure and can usefully be represented as graphs. Network analysis focuses on the relations among the nodes exploring the properts hies of network. We introduce a method for measuring the dependency between the nodes of a network, that is based on a structure in the local surroundings of the node. The approach extracts relations between the network's nodes and from either unweighted or already weighted network we get a weighted network where the assigned edge weights reflect the dependency between the nodes. Additionally, from dependency between the nodes, we derive a novel degree centrality measure which provides an interesting view on the importance of the node in a network. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/INCoS.2013.44 | INCoS |
Keywords | Field | DocType |
local dependency,interesting view,network structure,process eave,weighted network,assigned edge weight,local surrounding,network analysis,approach extracts relation,properts hies,novel degree centrality measure,graph theory | Network formation,Computer science,Network simulation,Computer network,Theoretical computer science,Artificial intelligence,Dynamic network analysis,Interdependent networks,Average path length,Evolving networks,Scale-free network,Weighted network,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sarka Zehnalova | 1 | 8 | 4.83 |
| Zdenek Horák | 2 | 28 | 3.48 |
| Milo Kudelka | 3 | 10 | 2.59 |
| Václav Snáel | 4 | 37 | 10.63 |