Modeling Topic-Partitioned Network Structure

Abstract:

In this talk, I will discuss two projects centered around modelling topic-partitioned network structure. The first focuses on obtaining and analyzing local government email corpora. I will describe a field experiment that we conducted to investigate whether governments' compliance with public records requests is influenced by the knowledge that their peers have already complied. I will then talk about studying local government organizations using the email data that we obtained via this experiment. I will present a new statistical topic model intended for discovering and summarizing topic-specific communication subnetworks. Focusing on the relationship between gender and professional communication networks, I will explain how this model can be used to explore persistent topic-specific network structures. The second project focuses on analyzing country—country interaction events of the form "country i took action a toward country j at time t." I will present a new Bayesian Poisson tensor factorization model, based on the Tucker decomposition, that learns latent country--community memberships, including the number of latent communities, as well as directed community--community interaction networks that are specific to "topics" of related actions types. I will show that our model infers interpretable communities and network structures that conform to our knowledge of international relations.

Biography:

Hanna Wallach is a Senior Researcher at Microsoft Research New York City and an Adjunct Associate Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst. She is also a member of UMass's Computational Social Science Institute. Hanna develops machine learning methods for analyzing the structure, content, and dynamics of social processes. Her work is inherently interdisciplinary: she collaborates with political scientists, sociologists, and journalists to learn how organizations work in practice by analyzing publicly available interaction data such as public record email networks, document dumps, press releases, meeting transcripts, and news articles. Hanna's research has had broad impact in machine learning, natural language processing, and the nascent field of computational social science. In 2010 her work on infinite belief networks won the best paper award at the AISTATS conference, in 2014 she was named one of Glamour magazine’s "35 Women Under 35 Who Are Changing the Tech Industry," and in 2015 she was elected to the Board of Trustees of the International Machine Learning Society. She is the recipient of several NSF grants, an IARPA grant, and a grant from the Office of Juvenile Justice and Delinquency Prevention. Hanna is committed to increasing diversity and has worked for over a decade to address the underrepresentation of women in computing. She founded two projects to increase women's involvement in free and open source software development, as well as the Women in Machine Learning Workshop, which is now in its eleventh year. Hanna holds a BA in computer science from the University of Cambridge, an MS in cognitive science and machine learning from the University of Edinburgh, and a PhD in machine learning from the University of Cambridge.