Machine Learning for Computational Social Science

Will Hamilton - PhD Candidate, Stanford University

Jan. 25, 2018, 10 a.m. - Jan. 25, 2018, 11 a.m.

McConnell 603

The combination of machine learning and massive social datasets has the potential to revolutionize our ability to predict and understand human behavior. However, machine learning on large social datasets is difficult because these datasets tend to be noisy, dynamic, and involve graph-structured relationships (e.g., social networks between users)—while traditional machine learning tools are largely designed for static datasets comprised of simple Euclidean vectors or grids.

In this talk, I will describe new methods that I have developed for machine learning on massive, graph-structured social datasets. The technical focus of the talk will be on techniques for graph embedding, i.e., representation learning on graph-structured data. In the first part, I will describe how I have used graph embedding techniques to enable diverse social applications—from modeling cultural change to predicting conflict between online communities. In the second part, I will describe a new graph embedding framework, called GraphSAGE, that can scale to datasets that are orders of magnitude larger than previous approaches and that has been deployed at the website Pinterest to power a recommender system serving over 200 million users. I will close the talk with a general outlook on social AI technologies, including future technical directions and important ethical considerations.

William (Will) Hamilton is a PhD Candidate in Computer Science at Stanford University, working jointly in the NLP and SNAP groups. He is co-advised by Dan Jurafsky and Jure Leskovec, and his interests lie at the intersection of machine learning, network science, natural language processing, and computational social science. Will's research is supported by the SAP Stanford Graduate Fellowship and an NSERC PGS-D Grant. Prior to coming to Stanford, Will completed a BSc and MSc at McGill University, where he studied machine learning under the supervision of Joelle Pineau and was the 2014 recipient of the Canadian AI Master's Thesis Award.