Overview:

Humans, as well as information, are organized in networks. Interacting with these networks is part of our daily lives. Thus, understanding, predicting and enhancing human behavior in networks poses important research problems for computer and data science with practical applications of high impact. Studying navigation patterns lets us understand better how people reason within complex networks and lets us build more intuitive information systems. I will present an algorithm for improving website hyperlink structure by mining raw web server logs. Producing links that are clicked 10-times more than average links.

Furthermore, communication through natural language is another prominent human behavior, presenting intriguing opportunities and challenges. I will discuss my work on person-to-person sentiment analysis in networks.

Bio:

Robert West is a sixth-year Ph.D. candidate in Computer Science in the Infolab at Stanford University, advised by Jure Leskovec. His research aims to understand, predict, and enhance human behavior in social and information networks by developing techniques in data science, data mining, network analysis, machine learning, and natural language processing. Previously, he obtained a Master's degree from McGill University in 2010 and a Diplom degree from Technische Universität München in 2007.