This thesis concerns Social Network Analysis as a mechanism for exploring Community Dynamics. To be able to use the Social Network methodologies, relationships existing between the modeling entities are required. In this thesis, we use two different kinds of relationships: e-mails exchanged and co-authorship of papers. The e-mails exchanged, as an indicator of information exchange in an organization, is used to facilitate the emergence of structure within the organization. In this thesis we demonstrate the effectiveness of using e-mail communication patterns for crisis detection in a hierarchically set organization. We compare the performance of a Social Network based Classifier with some of the traditional classifiers from the data mining framework for inferring this hierarchy. A generic framework for studying dynamic group transformations is presented and the co-authorship of papers, as an indicator of collaboration in an academic institution, is used to study the community behavioral patterns evolving over time. Enron e-mail corpus and the IISc Co-authorship Dataset are utilized for illustrative purposes.