Datasets originating from many different real-world domains can be represented in the form of interaction networks in a concise and meaningful fashion. Examples abound, ranging from gene expression networks to social networks, and from the World Wide Web to protein-protein interaction networks. The study of these complex interaction networks, which are often evolving, can provide insight into their structure, properties and behavior.
Identifying the portions of the network that are changing, characterizing the type of change and extracting relevant patterns that can help predict future events and behavior are all critical challenges that need to be met in this context. To this end the PI plans to explore and design an event-driven methodology to study the evolutionary behavior of such interaction networks from the perspective of node-level and community-level viewpoints. Incorporating semantic information and leveraging graph grammars in a structured manner will also be explored in this context.
The main scientific outcome of this research will include the ability to extract, analyze and understand key patterns and features of such dynamic interaction networks in the context of end applications drawn from clinical and social settings. The broader outcomes of this work will be to train capable graduate and undergraduate students in the fields of network analysis and data mining. Women and minorities will be especially encouraged to participate and existing interactions with a local HBCU will be strengthened.
Project Page: www.cse.ohio-state.edu/~srini/SGER/information
