GCN-based unsupervised community detection with refined structure centers and expanded pseudo-labeled set.
Community detection is a classical problem for analyzing the structures of various graph-structured data. An efficient approach is to expand the community structure from a few structure centers based on the graph topology. Considering them as pseudo-labeled nodes, graph convolutional network (GCN) i...
Saved in:
| Main Authors: | Bing Guo, Liping Deng, Tao Lian |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0327022 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
GCN-based weakly-supervised community detection with updated structure centres selection
by: Liping Deng, et al.
Published: (2024-12-01) -
Enhanced unsupervised domain adaptation with iterative pseudo-label refinement for inter-event oil spill segmentation in SAR images
by: Guangyan Cui, et al.
Published: (2025-05-01) -
Pseudo label refining for semi-supervised temporal action localization.
by: Lingwen Meng, et al.
Published: (2025-01-01) -
A unified approach for weakly supervised crack detection via affine transformation and pseudo label refinement
by: Zhongmin Huangfu, et al.
Published: (2025-03-01) -
Contrastive learning enhanced pseudo-labeling for unsupervised domain adaptation in person re-identification.
by: Xuemei Bai, et al.
Published: (2025-01-01)