Title: Exploiting Temporal Variations in Relational Domains
Speaker: Jennifer Neville

Abstract:
Many relational domains contain temporal information and dynamics that 
are important to model. As an example, consider scientific publication 
networks--paper publication events occur over time and coauthor 
relationships form and develop over time. The temporal aspects of the 
data can be used to identify relevant relationships and/or as an 
indication of relationship strength. For example, people that coauthor 
frequently are more likely to share research interests than people who 
coauthor infrequently. Also, a paper is more likely to share the topics 
of its references that were published in the recent past than those that 
were published in the distant past. Although many relational datasets 
contain this type of temporal information, past work in relational 
learning has focused primarily on modeling stati c of the 
data and has largely ignored temporal dynamics. In this work, we focus 
on modeling temporally-varying relationships in predictive models of 
attributes. By analyzing the temporal dynamics we aim to identify and 
emphasize more influential relationships, thus improving the performance 
of models that consider the characteristics of related entities during 
prediction. We present a framework that models dynamic relational data 
with a two-phase process, first summarizing the temporal-relational 
information with kernel smoothing, and then moderating attribute 
dependencies with the summarized relational information. We evaluate our 
approach on three real-world datasets and show that it results in 
significant performance gains compared to two baseline approaches that 
ignore the temporal aspects of the data.

Bio:
Jennifer Neville is an assistant professor at Purdue University with a 
joint appointment in the Departments of Computer Science and Statistics. 
She received her PhD in computer science from the University of 
Massachusetts Amherst in 2006. She has received a DARPA IPTO Young 
Investigator Award and is a current member of the DARPA Computer Science 
Study Group. Her research focuses on developing data mining and machine 
learning techniques for relational domains, including bioinformatics, 
citation analysis, fraud detection, and social network analysis.