Graph Transformer for Node Label Prediction with PyG

Updated on January 29, 2024 | in Data Science

In this tutorial, we attempt to identify the field of a paper based on its citation graph, which serves as a proxy for the paper’s content and its context within the scientific discourse. Understanding and leveraging this structure is central to our ML problem: how to infer the field of a paper from the complex web of citations in which it is embedded.

A Short Review on Graph Neural Networks: Mechanism and Implementation

Updated on September 14, 2022 | in 2. Knowledge Network / Network Science

Graphical Neural Networks (GNNs) are a family of neural networks that can operate naturally on graphically structured data. They provide an effective way to do node-level, edge-level, and graph-level prediction tasks by extracting and exploiting features from the underlying graph.

Study Notes | Stanford CS520: Knowledge Graph

Updated on August 10, 2021 | in 2. Knowledge Network / Network Science

Notes are shared publicly on github.

Graph Transformer for Node Label Prediction with PyG

A Short Review on Graph Neural Networks: Mechanism and Implementation

Study Notes | Stanford CS520: Knowledge Graph

Junyi Tao