Name
Abstract | ||
|---|---|---|
Combinatoric measures of entropy capture the complexity of a graph but rely upon the calculation of its independent sets, or collections of non-adjacent vertices. This decomposition of the vertex set is a known NP-Complete problem and for most real world graphs is an inaccessible calculation. Recent work by Dehmer et al. and Tee et al. identified a number of vertex level measures that do not suffer from this pathological computational complexity, but that can be shown to be effective at quantifying graph complexity. In this paper, we consider whether these local measures are fundamentally equivalent to global entropy measures. Specifically, we investigate the existence of a correlation between vertex level and global measures of entropy for a narrow subset of random graphs. We use the greedy algorithm approximation for calculating the chromatic information and therefore Korner entropy. We are able to demonstrate strong correlation for this subset of graphs and outline how this may arise theoretically. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/e20070481 | ENTROPY |
Keywords | Field | DocType |
graph entropy,chromatic classes,random graphs | Discrete mathematics,Graph,Mathematical optimization,Random graph,Vertex (geometry),Chromatic scale,Greedy algorithm,Graph entropy,Mathematics,Computational complexity theory | Journal |
Volume | Issue | ISSN |
20 | 7 | 1099-4300 |
Citations | PageRank | References |
1 | 0.36 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Philip Tee | 1 | 5 | 3.07 |
| George Parisis | 2 | 122 | 16.44 |
| Luc Berthouze | 3 | 155 | 26.14 |
| Ian Wakeman | 4 | 436 | 129.40 |