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To understand the structural dynamics of a large-scale social, biological or technological network, it may be useful to discover behavioral roles representing the main connectivity patterns present over time. In this paper, the authors propose a scalable non-parametric approach to automatically learn the structural dynamics of the network and individual nodes. Roles may represent structural or behavioral patterns such as the center of a star, peripheral nodes, or bridge nodes that connect different communities. Their novel approach learns the appropriate structural "Role" dynamics for any arbitrary network and tracks the changes over time. In particular, they uncover the specific global network dynamics and the local node dynamics of a technological, communication, and social network.
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