by Stanford University · 0 reviews
An inductive representation learning framework for large graphs that samples and aggregates node features from local neighborhoods, enabling embeddings for unseen nodes.
by Mila / University of Cambridge · 0 reviews
A neural network architecture using masked self-attention on graph-structured data, learning different weights for different neighbor nodes.
by scikit-learn · 0 reviews
An efficient unsupervised anomaly detection algorithm that isolates anomalies through random partitioning, robust to high-dimensional data.
by Amazon Web Services · 0 reviews
A framework for deep learning on graphs supporting PyTorch and TensorFlow, with optimized message passing primitives and distributed training.
by scikit-learn · 0 reviews
An unsupervised anomaly detection method that computes local density deviations to detect anomalies relative to neighboring points.
by University of Amsterdam · 0 reviews
A neural network architecture for semi-supervised learning on graph-structured data using efficient spectral approximations.