Math for Social Networks
Abstract
Social networks are recent phenomena whose pervasiveness has become undeniable. Critical information potentially can be extracted by both observing network state at any given instant as well as by monitoring network dynamics. Standard tools for examining network behavior typically target systems of communication or computer nodes, and evaluate context-relevant yet straightforward metrics such as connectivity. When dealing with social networks, the knowledge that can be distilled is potentially more useful, and hence an entirely new set of techniques must be developed. This thrust will develop new mathematical methods to facilitate more complete analysis of social networks while simultaneously constructing mechanisms by which this elevated understanding may be best communicated. This approach could comprise, e.g., i) the application of spatiotemporal signal processing techniques to monitoring network activity, with an emphasis on identifying precursors to undesirable events; and, ii) incorporating fundamentally that the component nodes are humans (or groups of humans), and hence interact in ways subject to psychosocial evaluation. By incorporating sophisticated signal processing while recognizing the defining role of the human agent, this thrust will change how social networks are monitored and analyzed. Hence, we recast social network analysis into a mathematical framework that captures the biological nature of the component nodes intrinsically and exploits this knowledge to produce a unique DoD capability.
Document Details
- Document Type
- Accomplishment
- Publication Date
- Oct 01, 2012
- Source ID
- eae357c35622f7aa8edb15adcfad9aff