visualizing communities and centralities from encounter traces
We have previously demonstrated that information about social relationships can yield improved performance when it is used to control epidemic forwarding. We believe that extensive work to model human connectivity -- incorporating notions of community and interaction 'weight' -- is required if we are to understand this phenomenon and build networks that capitalize on it. This paper describes a visualization of detected community structures uncovered by different methods from human encounter traces. We focus on extracting information related to levels of clustering, network transitivity, and strong community structure. The position change of hub nodes within the network is also visualized.
centrality clustering delay tolerant networks network measurement social networks