Identifying small subsets of agents for behavior tracking and abnormal event detection in dynamic networks
Joya A. Deri, Soummya Kar, Sérgio Pequito, José M. F. Moura · 2013
For very large dynamic networks, monitoring the behavior of a subset of agents provides an efficient framework for detecting changes in network topology. For example, in mobile caller networks with millions of subscribers, we would like to monitor the dynamics of the smallest possible set of subscribers and still be able to infer abnormal events that occur over the entire network. In general, we assume that the temporal behavior of a network agent is captured by a (local) dynamic state, which may reflect either a physical property such as the number of connections or an abstract quantity such as opinions or beliefs. Further, assuming coupled linear inter-agent dynamics in which the local agent states evolve as weighted linear combinations of the neighboring agents' states, we focus on tracking network-wide agent dynamics.