Models and Tools for Dynamic Health-Relevant Diffusion over Complex Networks

Peter Mucha and Jim Moody from the Department of Sociology at Duke have been awarded a new $1.8 million grant to study social networks and health. This study titled “Model and Tools for Dynamic Health-Relevant Diffusion over Complex Networks” from the Eunice Kennedy Shriver National Institute of Child Health and Human Development will support a collaboration between Duke and UNC over a five year period.

Designing the Roadmap for Social Network Data Management

carsey As part of the National Science Foundation’s Cyberinfrastructure Framework for 21st Century Science and Engineering (CIF21) Data Infrastructure Building Blocks (DIBBs) program, Thomas Carsey (PI) has been awarded a grant for the project “Designing the Roadmap for Social Network Data Management” to address the challenges of managing, archiving, and sharing social network data. Social network data provides unique data management challenges since this data often come from unstructured environments and as relationships spread within network data the storage and analytical memory requirements can grow exponentially. This project aims to bring together the social network analysis, information science, computer science, and data archive communities to develop a data infrastructure to support advanced analysis and research on social networks as well as to facilitate data sharing and archiving within this community.

Community Detection in Networks Across Time

muchaThe James S. McDonnell Foundation awarded Peter J. Mucha a six year grant for the project “Community Detection in Networks Across Time.”

While there exist a wealth of methods and studies on static network representations, the study of community detection in networks that vary over time has been relatively limited. Mucha et al. (2010) expanded the community detection concept known as modularity to networks that vary over time or which encode multiple different kinds of connections (multiplex networks). This multislice modularity methodology opens wide the door to numerous applications that were previously inaccessible to most community detection heuristics. At the same time, the modularity framework itself is well known to have a number of pitfalls, and there are many other well-known and well-studied methods for community detection of static (i.e. single-slice) networks.

Motivated by the successes of the multislice modularity approach, Mucha’s research program seeks to similarly extend other state-of-the-art community detection methods to multislice network data.