a computational framework to identify and characterize cell niches from spatial omics data at single-cell resolution
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Aug 11, 2025 - Jupyter Notebook
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graph-autoencoder
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A comprehensive (masked) graph autoencoders benchmark.
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Dec 4, 2024 - Python
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
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Jan 5, 2026 - Python
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.
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Nov 12, 2025 - Jupyter Notebook
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