Name
Abstract | ||
|---|---|---|
This work proposes a novel hybrid mixed-membership blockmodel (HMMB) that integrates three canonical network models to capture the characteristics of real-world interactions: community structure with mixed-membership, power-law-distributed node degrees, and sparsity. This hybrid model provides the capacity needed for realism, enabling control and inference on individual attributes of interest such... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TNSE.2018.2823324 | IEEE Transactions on Network Science and Engineering |
Keywords | Field | DocType |
Hidden Markov models,Atmospheric modeling,Stochastic processes,Mathematical model,Inference algorithms,Bayes methods,Data models | Data mining,Data modeling,Mathematical optimization,Inference,Identifiability,Fisher information,Initialization,Hidden Markov model,Mathematics,Network model,Bayesian probability | Journal |
Volume | Issue | ISSN |
6 | 3 | 2327-4697 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Edward K. Kao | 1 | 123 | 10.06 |
| Steven T. Smith | 2 | 21 | 5.77 |
| Edoardo Airoldi | 3 | 709 | 59.54 |