A Regularized Graph Layout Framework for Dynamic Network Visualization

Many real-world networks, including social and information networks, are dynamic structures that evolve over time. Such dynamic networks are typically visualized using a sequence of static graph layouts. In addition to providing a visual representation of the network structure at each time step, the sequence should preserve the mental map between layouts of consecutive time steps to allow a human to interpret the temporal evolution of the network. In this paper, we propose a framework for dynamic network visualization in the on-line setting where only present and past graph snapshots are available to create the present layout. The proposed framework creates regularized graph layouts by augmenting the cost function of a static graph layout algorithm with a grouping penalty, which discourages nodes from deviating too far from other nodes belonging to the same group, and a temporal penalty, which discourages large node movements between consecutive time steps. The penalties increase the stability of the layout sequence, thus preserving the mental map. We introduce two dynamic layout algorithms within the proposed framework, namely dynamic multidimensional scaling (DMDS) and dynamic graph Laplacian layout (DGLL). We apply these algorithms on several data sets to illustrate the importance of both grouping and temporal regularization for producing interpretable visualizations of dynamic networks.Comment: To appear in Data Mining and Knowledge Discovery, supporting material (animations and MATLAB toolbox) available at http://tbayes.eecs.umich.edu/xukevin/visualization_dmkd_201

A New Approach to Infer Changes in the Synchronization of Business Cycle Phases

This paper proposes a Markov-switching framework useful to endogenously identify regimes where economies enter recessionary and expansionary phases synchronously, and regimes where economies are unsynchronized following independent business cycle phases. The reliability of the framework to track synchronization changes is corroborated with Monte Carlo experiments. An application to the case of U.S. states reports substantial changes over time in the cyclical affiliation patterns of states. Moreover, a network analysis discloses a change in the propagation pattern of aggregate contractionary shocks across states, suggesting that regional economies in U.S. have become more interdependent since the early 90s

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. In this dissertation, I propose new computational methods for machine learning and statistical inference on dynamic networks with time-evolving structures. Specifically, I develop methods for visualization, tracking, clustering, 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 methods proposed in this dissertation, 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.PHDElectrical Engineering-SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/94022/1/xukevin_1.pd