Name
Abstract | ||
|---|---|---|
Markov chains (MCs) are convenient means of generating realizations of networks with a given (joint or otherwise) degree distribution (DD), since they simply require a procedure for rewiring edges. The major challenge is to find the right number of steps to run such a chain, so that we generate truly independent samples. Theoretical bounds for mixing times of these MCs are too large to be practica... |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1093/comnet/cnu041 | Journal of Complex Networks |
Keywords | Field | DocType |
graph generation,Markov chain Monte Carlo,independent samples | Discrete mathematics,Combinatorics,Random graph,Graph property,Empirical evidence,Computer science,Markov chain,Proportionality (mathematics),Degree distribution | Journal |
Volume | Issue | ISSN |
3 | 2 | 2051-1310 |
Citations | PageRank | References |
4 | 0.44 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jaideep Ray | 1 | 198 | 24.42 |
| Ali Pinar | 2 | 897 | 64.46 |
| C. Seshadhri | 3 | 936 | 61.33 |