Stanford CS224W: Machine Learning with Graphs - Stanford CS224W: ML with Graphs | 2021 | Lecture 15.2 - Graph RNN: Generating Realistic Graphs

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GBr7ec Jure Leskovec Computer Science, PhD Here we provide an in-depth discussion of GraphRNN, one of the first deep generative models for graphs. The idea of GraphRNN is to generate graphs via sequentially adding nodes and edges. This graph generation process can be modeled in 2 levels: (1) node-level, where each node is added at a time; (2) edge-level, where edges are added between a new node and the existing nodes. We model these 2 processes via Recurrent Neural Networks (RNNs). After describing how an RNN can be used for generating sequences, we describe the detailed algorithm for GraphRNN training and inference. More information can be found in the paper: “GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models” https://arxiv.org/abs/1802.08773 To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224w/ #machinelearning #machinelearningcourse