This book presents a model and a tool for discovering and analyzing scientific communities. Several approaches have been proposed to discover community structure applying clustering methods over different networks. However, most existing approaches do not allow for overlapping of communities, which are instead natural when we consider communities of scientists. The approach presented in this book combines different clustering algorithms for detecting overlapping scientific communities, based on conference publication data. Moreover, community based evaluation metrics are proposed for measuring the scientific productivity and impact of researches and researchers. The Community Engine Tool (CET) implements the algorithm and was evaluated using the DBLP dataset, which contains information on more than 12 thousand conferences. The results show that using our approach it is possible to automatically produce community structure close to human-defined classification of conferences. The approach is part of a larger research effort aimed at studying how scientific communities are born, evolve, remain healthy or become unhealthy (e.g., self-referential), and eventually vanish.