With the program HistCite™ it is possible to generate and visualize the most relevant papers in a set of documents retrieved from the Science Citation Index. Historical reconstructions of scientific developments can be represented chronologically as developments in networks of citation relations extracted from scientific literature. This study aims to go beyond the historical reconstruction of scientific knowledge, enriching the output of HistCite™ with algorithms from social-network analysis and information theory. Using main-path analysis, it is possible to highlight the structural backbone in the development of a scientific field. The expected information value of the message can be used to indicate whether change in the distribution (of citations) has occurred to such an extent that a path-dependency is generated. This provides us with a measure of evolutionary change between subsequent documents. The “forgetting and rewriting” of historically prior events at the research front can thus be indicated. These three methods—HistCite, main path and path dependent transitions—are applied to a set of documents related to fullerenes and the fullerene-like structures known as nanotubes.