Information networks are prevalent in society. An understanding of the
properties of such networks including their structure and inherent
relationships can serve to assist knowledge and discovery.
In this work we examine ways in which information networks assist
in creating and organizing knowledge. We first describe an environment
for mathematical knowledge management. This open information system
impacts the formal methods community by making data management
central and allows for collaborative creation and sharing of mathematics.
The architecture of that system lays the foundation for a prototype Formal
Digital Library (FDL), which serves to aggregate mathematical resources
and provide data-rich services across them. The foremost importance of
the FDL in this dissertation, however is that it serves as a laboratory for
examining information networks in general. Because of the rich structure
inherent in formal mathematics, it is a well-suited domain for testing and
evaluating network analysis techniques and their roles in knowledge
discovery. We examine the structural properties of the FDL’s contents
and also the recent definitive proof of the four color theorem by Gonthier.
Our analysis reveals a characteristic depth and breadth and uses
Kleinberg’s HITS algorithm to reveal (mathematical) hubs and
authorities. To show generality, we also examine non-mathematical and
dynamic networks. In particular, we build institute and country based
collaboration networks from over 200,000 scholarly publications in the
physics community to model long distance collaboration trends over 30
years. The findings demonstrate the influence of graph-theoretic metrics
and visualizations on discovery.
Finally, we expand our notion of links in a network and describe
concepts and methods for linking data to published articles to support
quality and authority. We describe our experimentation with a new
authoring mechanism that incorporates data provenance to provide
evidence for claims made in articles. Because math in articles is
something that can be ambiguous and sensitive to errors, we see the
mathematics domain as an ideal candidate for this work. However, our
concepts also generalize to other domains where the accurateness and
archival of data referred to in an article is of importance.