The rapidly expanding volume of scientific literature across a wide range of scientific fields and disciplines makes the task of distilling valuable knowledge ever more difficult.  To overcome the drawbacks of current techniques for evaluating large bodies of scientific knowledge a Drexel researcher, Dr. Chaomei Chen, has developed a suite of advanced algorithms  embodied in a software system called CiteSpace.  CiteSpace is a Java application for visualizing and analyzing trends and patterns in scientific literature. In particular, CiteSpace provides various functions to facilitate the understanding and interpretation of network patterns and historical patterns, including identifying the fast-growth topical areas, finding citation hotspots, decomposing a network into clusters, automatic labeling clusters with terms from citing articles and geospatial patterns of collaboration.  CiteSpace supports structural and temporal analyses of a variety of networks derived from scientific publications, including collaboration networks, author co-citation networks, and document co-citation networks. It also supports networks of hybrid node types such as terms, institutions, and countries, and hybrid link types such as co-citation, co-occurrence, and directed citing links.