EAGER: Asynchronous Event Models for State-Topology Co-Evolution of Temporal Networks

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

The purpose of this research project is to develop probabilistic models and the related machine learning algorithms for modeling network evolution and dynamics. The research lays theoretic foundations and provides practical tools for scientists to control networks in order to achieve desirable outcomes. Although the research is widely applicable, the research team primarily considers two application areas: social networks and P2P microfinance. In social networks, this project brings practical values to the Internet industry by better understanding and modeling of user behaviors and their impacts on social ties and social group formation. For P2P microfinance, this project has the potential to better engage not-for-profit lenders and thus to help small business in developing countries. Furthermore, the research provides materials and contents for both undergraduate and graduate education and helps students develop interdisciplinary mindsets and tools needed to tackle real-world problems. This proposed research aims to develop machine learning theory and algorithms for networked asynchronous and interdependent event streams arising from modern applications. The researchers especially emphasize methodology that can handle temporal networks when the underlying network structures are undergoing substantial changes. One major theme of the proposal is the modeling of the interplay between network node dynamics and network topology dynamics, or network co-evolution. The researchers propose a novel framework based on multivariate point processes for modeling and analyzing event data. The methods significantly expand the application area of conventional machine learning techniques. One example is to answer the question ``who will do what and when'', which is critical to event sequence modeling in network data analysis where traditional machine learning algorithms are difficult to apply.