Social Network Analysis
Spatio-Temporal Informatics for Networks and Graphs (STING) refers to the mechanism by which semantic data—data or entities that are related in various ways—are processed for analysis. Understanding the complex dynamics of entity networks can provide the key to unlocking critical information about behaviors in various types of communities.
At PNNL's Center for Adaptive Supercomputing Software-Multithreaded Architectures (CASS-MT), researchers are developing methods for analyzing massive complex semantic networks. More thoroughly understanding social networks, for example, can help tackle challenges such as influencing change and understanding critical trends in behaviors and customs. Similar computational capabilities can be applied to finding vulnerabilities in the power grid and monitoring important protein interactions in cancer research.
In the context of social networks, for instance, Facebook represents a network of over 500 million active users. Within this overarching framework, people are connected to other people, who are connected to other people, and so on, and underlying communities develop based on other parameters such as age, geographic location, interests, or lifestyle indicators. As people are removed from an extended social network, or their other parameters change, their associated communities are also transformedâ€”constantly evolving the network landscape.
David A. Bader, Task Lead, Georgia Tech
Jason Riedy, Georgia Tech
David Ediger, Georgia Tech