Name
Abstract | ||
|---|---|---|
Community detection is an important task in network analysis, in which we aim to learn a network partition that groups together vertices with similar community-level connectivity patterns. By finding such groups of vertices with similar structural roles, we extract a compact representation of the network's large-scale structure, which can facilitate its scientific interpretation and the prediction... |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1093/comnet/cnu026 | Journal of Complex Networks |
Keywords | Field | DocType |
community detection,weighted relational data,block models,exponential family,variational Bayes | Network partition,Discrete mathematics,Block structure,Vertex (geometry),Exponential family,Posterior probability,Stochastic block model,Artificial intelligence,Network analysis,Machine learning,Mathematics,Bayesian probability | Journal |
Volume | Issue | ISSN |
3 | 2 | 2051-1310 |
Citations | PageRank | References |
38 | 1.49 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christopher Aicher | 1 | 46 | 3.48 |
| Abigail Z. Jacobs | 2 | 93 | 5.83 |
| Aaron Clauset | 3 | 2033 | 146.18 |