Stanford CS224W Graph ML Tutorials

By Alexander Hurtado + Aditya Iswara as part of the Stanford CS224W course project (Fall 2021)

An introduction to explainability methods for GNNs

By Paridhi Maheshwari, Jian Vora, Sharmila Reddy Nangi as part of the Stanford CS 224W course project.

A learned solution to node-level classification on irregular graphs via graph neural networks.

By Julie Wang, Amelia Woodward, Tracy Cai as part of the Stanford CS224W course project.

Recommending Spotify Playlist Tracks with Neural Collaborative Filtering Using Graph Machine Learning

Interactions are everywhere, from social dynamics to physical systems. Learn how to predict interactions using graph neural networks

Learn how to write a graph neural network for session-based recommendations

Graph learning techniques are hard to add to existing ML pipelines. Using Correct&Smooth to refine your existing predictions by “smoothing”…

By Derrick Li, Peter Maldonado, Akram Sbaih as part of the Stanford CS224W (Machine course project.

Visual Question Answering requires understanding and relating text and image inputs. Here we use Graph Neural Networks to reason over both…

By Siddharth Doshi and Olamide Abiose as part of the Stanford CS224W course project.