Abstract

Current social network analytic methods analyze a static aggregate graph, which provides a limited view of the structure and behavior of real world social networks. Real world networks are dynamic they evolve over time as new connections form between individuals, and networks themselves act as a substrate for the flow of information and influence. Ignoring dynamics can produce a distorted, and even wrong, view of who the important individuals are in a social network, what is the nature and strength of the connections between them, and what are the communities of similar or similarly behaving individuals. The erroneous conclusion reached by static network analysis will waste analysts time and resources. For these reasons, we developed network analysis methods that directly incorporate time. The research had two major threads -Understand how networks evolve over time, and how changes in topology affect evolution of influence and groups -Understand the impact of dynamics and network flows on the measurement of the network structure.

Date

April 1, 2015

Authors

Kristina Lerman

Issue

AFRLRIRSTR2015077