Name
Title | ||
|---|---|---|
Integrating overlapping community discovery and role analysis: Bayesian probabilistic generative modeling and mean-field variational inference. |
Abstract | ||
|---|---|---|
The joint modeling of community discovery and role analysis was shown useful to explain, predict and reason on network topology. Nonetheless, earlier research on the integration of both tasks suffers from major limitations. Foremost, a key aspect of role analysis, i.e., the strength of role-to-role interactions, is ignored. Moreover, two fundamental properties of networks are disregarded, i.e., heterogeneity in the connectivity structure of communities and the growing link probability with node involvement in common communities. Additionally, scalability with network size is limited. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.engappai.2019.103437 | Engineering Applications of Artificial Intelligence |
Keywords | Field | DocType |
Overlapping community discovery,Role analysis,Link explanation and prediction,Generative probabilistic modeling,Bayesian network analysis | Random variable,Computer science,Inference,Network topology,Artificial intelligence,Probabilistic logic,Poisson distribution,Prior probability,Machine learning,Bayesian probability,Scalability | Journal |
Volume | ISSN | Citations |
89 | 0952-1976 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gianni Costa | 1 | 235 | 24.04 |
| Riccardo Ortale | 2 | 282 | 27.46 |