  Research grants have played an important role in seeding and promoting
fundamental research projects worldwide. There is a growing demand for
developing and delivering scientific influence analysis as a service on
research grant repositories. Such analysis can provide insight on how research
grants help foster new research collaborations, encourage cross-organizational
collaborations, influence new research trends, and identify technical
leadership. This paper presents the design and development of a grants-based
scientific influence analysis service, coined as GImpact. It takes a
graph-theoretic approach to design and develop large scale scientific influence
analysis over a large research-grant repository with three original
contributions. First, we mine the grant database to identify and extract
important features for grants influence analysis and represent such features
using graph theoretic models. For example, we extract an institution graph and
multiple associated aspect-based collaboration graphs, including a discipline
graph and a keyword graph. Second, we introduce self-influence and co-influence
algorithms to compute two types of collaboration relationship scores based on
the number of grants and the types of grants for institutions. We compute the
self-influence scores to reflect the grant based research collaborations among
institutions and compute multiple co-influence scores to model the various
types of cross-institution collaboration relationships in terms of disciplines
and subject areas. Third, we compute the overall scientific influence score for
every pair of institutions by introducing a weighted sum of the self-influence
score and the multiple co-influence scores and conduct an influence-based
clustering analysis. We evaluate GImpact using a real grant database,
consisting of 2512 institutions and their grants received over a period of 14
years...
