Computational Methods for Learning and Inference on Dynamic Networks

Networks are ubiquitous in science, serving as a natural
representation for many complex physical, biological, and social
phenomena. Significant efforts have been dedicated to analyzing such
network representations to reveal their structure and provide some
insight towards the phenomena of interest. Computational methods for
analyzing networks have typically been designed for static networks,
which cannot capture the time-varying nature of many complex
phenomena.

I propose several new computational methods for machine learning and
statistical inference on dynamic networks with time-evolving
structures. Specifically, I develop methods for visualization,
tracking, and prediction of dynamic networks. The proposed methods
take advantage of the dynamic nature of the network by intelligently
combining observations at multiple time steps. This involves the
development of novel statistical models and state-space
representations of dynamic networks. Using the proposed methods, I
identify long-term trends and structural changes in a variety of
dynamic network data sets including a social network of spammers and a
network of physical proximity among employees and students at a
university campus.