Recent advances in Graph Convolutional Neural Networks (GCNNs) have shown their efficiency for nonEuclidean data on graphs, which often require a large amount of labeled data with high cost. It it ...

Learn the fundamentals of graph theory for data science and machine learning Implement state-of-the-art graph neural network architectures Build creative and powerful applications in various fields ...

Modelling learning objects (LO) within their context enables the learner to advance from a basic, remembering-level, learning objective to a higher-order one, i.e., a level with an application- and ...

TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for Fine-grained Encrypted Traffic Classification Official implementation of the WWW'23 research paper: TFE-GNN: A Temporal Fusion ...

Agile rituals are great, but without tracking constraints and dependencies live, your transformation will stall.

Missense variants in the O-GlcNAc transferase ( OGT) gene have recently been shown to segregate with a syndromic form of intellectual disability (OGT-ID), underscoring the importance of protein ...

Using interactive charts, see how mortgage rates have changed over time and impacted the housing market in modern history.