Stanford Libraries' official online search tool for books, media, journals, databases, government documents and more. When you complete a course, you’ll be eligible to receive a shareable electronic Course Certificate for a small fee. Link Analysis: PageRank : Thu Oct 31: 12. Graph Convolutional Networks (GCN) Traditionally, neural networks are designed for fixed-sized graphs. 7. Deep Generative Models for Graphs : Tue Oct 29: 11. In particular, we model polypharmacy side effects. The course will cover recent research on the structure and analysis of such large social and information networks and on models and algorithms that abstract their basic properties. Stanford Memes Group: Network Construction, Community Detection, and Link Prediction Improving Recall and Precision in Graph Convolutional Networks for Node Classification using Node2Vec Embeddings Leveraging Network Structures to Reveal Obfuscated and Hidden Attributes in Google+ Networks Take courses from the world's best instructors and universities. Graph Neural Networks.

However, most of the graphs in the real world have an arbitrary size and complex topological structure. 4 Graph Neural Networks Graph Neural Networks, or GNNs, denote a class of neural networks that implement functions of the form ˝(G;n) 2 R m which map a graph Gand one of its nodes into an m-dimensional Euclidean space. Decagon handles multimodal graphs with large numbers of edge types. G that helps predict the label of an entire graph, y G = g(h G). Decagon's graph convolutional neural network (GCN) model is a general approach for multirelational link prediction in any multimodal network. Graph Representation Learning : Thu Oct 17: 8. Students will explore how to practically analyze large-scale network data and how to reason about it through models for network structure and evolution. Graph Neural Networks : Project Proposal due: Tue Oct 22: 9. Here we specifically focus on using Decagon for computational pharmacology. Network Effects and Cascading Behavior : Homework 3 out Graph Neural Networks: Hands-on Session [Colab Notebook] Thu Oct 24: 10. For example, we could consider an image as a grid graph or a piece of text as a line graph. Courses include recorded auto-graded and peer-reviewed assignments, video lectures, and community discussion forums. Introduction to graph neural networks in SearchWorks catalog Skip to search Skip to main content