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Slide link: http://snap.stanford.edu/class/cs224w-2018/handouts/09-node2vec.pdf

Techniques for deep learning on network/graph structed data (e.g., graph convolutional networks and GraphSAGE). Part 3: Applications . Applications of network representation learning for recommender systems and computational biology. Biographies. All the organizers are members of the SNAP group under Prof. Jure Leskovec at Stanford University.

He is the recipient of the SAP Stanford Graduate Fellowship, an Alexander Graham Bell Graduate Scholarship, and his work has been covered in the New York Times, Wired, and the BBC. He is the co-lead developer of GraphSAGE, a state-of-the-art open-source framework for NRL. Rex Ying is a PhD Candidate in Computer Science at Stanford University. His research focuses on deep learning algorithms for network-structured data, and applying these methods in domains including recommender systems ...

Stanford University Abstract Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction.

Sep 16, 2021 · Overview. Graphs are emerging as an abstraction to represent complex data, such as social networks, knowledge graphs, molecular graphs, biomedical networks, as well as for modeling 3D objects, manifolds, and source code. Machine learning, especially deep representation learning, on graphs is an emerging field with a wide array of applications from protein folding and fraud detection, to drug discovery and recommender systems.

Representation Learning on Graphs: Methods and Applications William L. Hamilton wleif@stanford.edu Rex Ying rexying@stanford.edu Jure Leskovec jure@cs.stanford.edu Department of Computer Science Stanford University Stanford, CA, 94305 Abstract Machine learning on graphs is an important and ubiquitous task with applications ranging from drug

Representation Learning on Graphs: Methods and Applications William L. Hamilton wleif@stanford.edu Rex Ying rexying@stanford.edu Jure Leskovec jure@cs.stanford.edu Department of Computer Science Stanford University Stanford, CA, 94305 Abstract Machine learning on graphs is an important and ubiquitous task with applications ranging from drug

Handling Missing Data with Graph Representation Learning Jiaxuan You1 Xiaobai Ma 2Daisy Yi Ding3 Mykel Kochenderfer Jure Leskovec1 1Department of Computer Science, 2Department of Aeronautics and Astronautics, and 3Department of Biomedical Data Science, Stanford University {jiaxuan, jure}@cs.stanford.edu {maxiaoba, dingd, mykel}@stanford.edu Abstract

03.10.2019 · Slide link: http://snap.stanford.edu/class/cs224w-2018/handouts/09-node2vec.pdf

Check out the freely-available Stanford course Machine Learning with Graphs, taught by Jure Leskovec, and see how a world renowned researcher ...

Graph Representation Learning Jiaxuan You1 Xiaobai Ma 2Daisy Yi Ding3 Mykel Kochenderfer Jure Leskovec1 1Department of Computer Science, 2Department of Aeronautics and Astronautics, and 3Department of Biomedical Data Science, Stanford University {jiaxuan, jure}@cs.stanford.edu {maxiaoba, dingd, mykel}@stanford.edu Abstract

This course focuses on the computational, algorithmic, and modeling challenges specific to the analysis of massive graphs. By means of studying the underlying ...

Machine learning, especially deep representation learning, on graphs is an emerging field with a wide array of applications from protein folding and fraud detection, to drug discovery and recommender systems. In the Stanford Graph Learning Workshop, we will bring together ...

Stanford to offer Free Machine Learning with Graphs course online from fall ... The course focuses on computational, algorithmic, and modelling ...

Motivation