Computer Science

Colloquium Details

Human Behavior in Networks

Abstract

Humans as well as information are organized in networks. Interacting with these networks is part of our daily lives: we talk to friends in our social network; we find information by navigating the Web; and we form opinions by listening to others and to the media. Thus, understanding, predicting, and enhancing human behavior in networks poses important research problems for computer and data science with practical applications of high impact.

Navigation constitutes one fundamental human behavior: in order to make use of the information and resources around us, we constantly explore, disentangle, and navigate networks such as the Web. Studying human navigation contributes to our scientific understanding of how humans reason about complex networks and lets us build more human-friendly information systems. Here I analyze navigation traces collected through a human-computation game, revealing basic human navigation strategies, and then harness these insights in an algorithm for improving website hyperlink structure by mining raw web server logs. The system is being deployed on Wikipedia's full server logs at terabyte scale, producing links that are clicked 10 times as frequently as the average link added by Wikipedia editors.

Communication and coordination through natural language is another prominent human network behavior. Studying the interplay of network structure and language has the potential to benefit both sociolinguistics and natural language processing. Intriguing opportunities and challenges have arisen recently with the advent of online social media, which produce large amounts of both network and natural-language data. As an example, I will discuss my work on person-to-person sentiment analysis in networks, which combines the sociological theory of structural balance with techniques from natural language processing, resulting in a sentiment prediction model that clearly outperforms both text-only and network-only versions.

I will conclude the talk by sketching interesting future directions for computational approaches to studying human behavior in networks.

Biography

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.