Name
Abstract | ||
|---|---|---|
We propose a method for simultaneously detecting shared and unshared communities in heterogeneous multilayer weighted and undirected networks. The multilayer network is assumed to follow a generative probabilistic model that takes into account the similarities and dissimilarities between the communities. We make use of a variational Bayes approach for jointly inferring the shared and unshared hidden communities from multilayer network observations. We show that our approach outperforms state-of-the-art algorithms in detecting disparate (shared and private) communities on synthetic data as well as on real genome-wide fibroblast proliferation dataset. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/icassp.2019.8683574 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
multilayer networks, community detection, heterogeneous communities, variational Bayes | Pattern recognition,Computer science,Synthetic data,Statistical model,Artificial intelligence,Generative grammar,Machine learning,Bayes' theorem | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hafiz Tiomoko Ali | 1 | 5 | 2.57 |
| Sijia Liu | 2 | 181 | 42.37 |
| Yasin Yilmaz | 3 | 195 | 25.95 |
| Romain Couillet | 4 | 692 | 74.03 |
| Indika Rajapakse | 5 | 1 | 1.74 |
| Alfred O. Hero III | 6 | 2600 | 301.12 |