Recent innovations have resulted in a plethora of social applications on the Web, such as blogs, social networks, and community photo and video sharing applications. Such applications can typically be represented as evolving interaction graphs with nodes denoting entities and edges representing their interactions. The study of entities and communities and how they evolve in such large dynamic graphs is both important and challenging.

While much of the past work in this area has focused on static analysis, more recently researchers have investigated dynamic analysis. In this paper, in a departure from recent efforts, we consider the problem of analyzing patterns and critical events that affect the dynamic graph from the viewpoint of a single node, or a selected subset of nodes. Defining and extracting a relevant viewpoint neighborhood efficiently, while also quantifying the key relationships among nodes involved are the key challenges we address. We also examine the evolution of viewpoint neighborhoods for different entities over time to identify key structural and behavioral transformations that occur.