A comprehensive (masked) graph autoencoders benchmark.
-
Updated
Apr 16, 2026 - Python
#
graph-autoencoder
Here are 5 public repositories matching this topic...
This project detects structural network anomalies using a GNN autoencoder. It contrasts this deep learning approach with the classic DBSCAN method. While DBSCAN only uses node features (CPU, RAM), the GNN learns the graph's topology to identify statistically improbable links, proving superior for structural analysis.
deep-learning ml pytorch network-engineering pytorch-geometric gnn dbscan-clustering-algorithm graph-autoencoder structural-anomaly-detection
-
Updated
Jan 24, 2026 - Python
Reconstructed research implementation of graph autoencoder anomaly detection with GAT and MST-geodesic regularization.
machine-learning reproducible-research pytorch anomaly-detection graph-neural-networks pytorch-geometric graph-autoencoder
-
Updated
Sep 21, 2026 - Python
Multi-scale latent representation learning and autoencoding for molecular graphs.
-
Updated
Sep 22, 2026 - Python
교통 CCTV 이상징후 연구 코드 — 규칙·시계열·그래프 모델의 판정 흐름
-
Updated
Sep 17, 2026 - Python
Add this topic to your repo
To associate your repository with the graph-autoencoder topic, visit your repo's landing page and select "manage topics."