graph-autoencoder

a computational framework to identify and characterize cell niches from spatial omics data at single-cell resolution

A comprehensive (masked) graph autoencoders benchmark.

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.

This project models the GitHub collaboration network to predict potential partnerships. It applies Social Network Analysis (SNA) and a Graph Autoencoder (GAE) to tackle the link prediction task, identifying future collaborators with high accuracy.

Unsupervised anomaly detection on the Elliptic Bitcoin transaction Graph using Isolation Forest, PCA, LOF, a GCN-based Graph Autoencoder and Ensemble scoring.

A comprehensive bridge impact analysis system combining heterogeneous graph neural networks (HGNN) for closure-impact prediction with graph autoencoders (GAE/VGAE/HetVGAE) for unsupervised bridge similarity learning. This extends the system with heterogeneous graph variational autoencoders for metapath-based bridge classification.

Deep learning for systemic financial risk: an Unsupervised Graph Autoencoder and a Spatio-Temporal GNN (GCN+LSTM) trained on 26 years of BIS banking network data (2000–2026) to autonomously detect crises and forecast cross-border exposure shifts — no manual features, no crisis labels.

STGAE 기반 교통사고 이상징후 탐지 — Spatio-Temporal Graph Autoencoder, 4-Level Anomaly Engine

This repository contains the Hopf-GAE, a physics-informed deep learning architecture that detects depression-related dynamical abnormalities without ever training on depressed brains.