Representing and Analyzing Pathway Data Through Networks
Disseration Proposal
Past Event
Timothy LaRock
Network Science PhD Student
Feb 25, 2020
Watch video
9:30 am
177 Huntington Ave
11th floor

The broad aim of my dissertation research is to address representation of network data in two ways. First, I address incomplete network data. Incomplete data is a problem when studying most real-world systems because data is either collected with imperfect sensors or by sampling from the system. I investigate strategies to extend samples of incomplete network data through node querying to get a more accurate representation of the system and thus improve any analysis done on the data. Second, I study the representation of sequences of interactions, also understood as pathways through a network. Network Scientists often aggregate such sequential data into a traditional weighted network representation, but this can destroy information about the system by ignoring correlations in how a network is traversed. I work with representations that encode pathway correlations and develop methodology for their analysis. My research can be applied broadly, from the study of web navigation and information seeking, to human movements through transportation systems, to understanding the global network of container shipping.

About the speaker
Tim is a fourth-year doctoral student advised by Professor Tina Eliassi-Rad. His work falls at the intersection of network science, data mining and machine learning. In particular, Tim’s research seeks to identify and understand sequential patterns and dependencies in network data, such as passenger movement through public transit systems, goods through logistics networks, or users navigating the Web. He also develops machine learning methods for improving partially observed network data through API querying. Prior to joining the Institute in 2016, Tim completed a B.S. in Computer Science and Applied Mathematics with a minor in Philosophy at the State University of New York at Albany, where he conducted research on load balancing in cellular networks and unsupervised transmitter detection in wireless frequency spectrum data, under the supervision of Professor Petko Bogdanov and Professor Mariya Zheleva.