Modularity-aware graph autoencoders for joint community detection and link prediction.
- DOI
- 10.1016/j.neunet.2022.06.021
- Published
- 2022 Sep
- Container
- Neural networks : the official journal of the International Neural Network Society
- Publisher
- Not recorded
- Open access
- no
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Cite this work
BibTeX
@article{allodium:10.1016/j.neunet.2022.06.021,
title = {Modularity-aware graph autoencoders for joint community detection and link prediction.},
author = {Salha-Galvan G and Lutzeyer JF and Dasoulas G and Hennequin R and Vazirgiannis M},
year = {2022},
journal = {Neural networks : the official journal of the International Neural Network Society},
doi = {10.1016/j.neunet.2022.06.021},
url = {https://doi.org/10.1016/j.neunet.2022.06.021}
}RIS
TY - JOUR TI - Modularity-aware graph autoencoders for joint community detection and link prediction. AU - Salha-Galvan G AU - Lutzeyer JF AU - Dasoulas G AU - Hennequin R AU - Vazirgiannis M PY - 2022 JO - Neural networks : the official journal of the International Neural Network Society DO - 10.1016/j.neunet.2022.06.021 UR - https://doi.org/10.1016/j.neunet.2022.06.021 ER -
APA
G, S., JF, L., G, D., R, H., & M, V. (2022). Modularity-aware graph autoencoders for joint community detection and link prediction.. Neural networks : the official journal of the International Neural Network Society. https://doi.org/10.1016/j.neunet.2022.06.021
Source records
- pubmed · retrieved 2026-09-25T15:45:01.056Z
- europe-pmc · retrieved 2026-09-25T15:45:01.104Z
- hal · retrieved 2026-09-25T15:45:01.105Z