19 research outputs found
Analysing Scientific Collaborations of New Zealand Institutions using Scopus Bibliometric Data
Scientific collaborations are among the main enablers of development in small
national science systems. Although analysing scientific collaborations is a
well-established subject in scientometrics, evaluations of scientific
collaborations within a country remain speculative with studies based on a
limited number of fields or using data too inadequate to be representative of
collaborations at a national level. This study represents a unique view on the
collaborative aspect of scientific activities in New Zealand. We perform a
quantitative study based on all Scopus publications in all subjects for more
than 1500 New Zealand institutions over a period of 6 years to generate an
extensive mapping of scientific collaboration at a national level. The
comparative results reveal the level of collaboration between New Zealand
institutions and business enterprises, government institutions, higher
education providers, and private not for profit organisations in 2010-2015.
Constructing a collaboration network of institutions, we observe a power-law
distribution indicating that a small number of New Zealand institutions account
for a large proportion of national collaborations. Network centrality concepts
are deployed to identify the most central institutions of the country in terms
of collaboration. We also provide comparative results on 15 universities and
Crown research institutes based on 27 subject classifications.Comment: 10 pages, 15 figures, accepted author copy with link to research
data, Analysing Scientific Collaborations of New Zealand Institutions using
Scopus Bibliometric Data. In Proceedings of ACSW 2018: Australasian Computer
Science Week 2018, January 29-February 2, 2018, Brisbane, QLD, Australi
The Bayan Algorithm: Detecting Communities in Networks Through Exact and Approximate Optimization of Modularity
Community detection is a classic problem in network science with extensive
applications in various fields. Among numerous approaches, the most common
method is modularity maximization. Despite their design philosophy and wide
adoption, heuristic modularity maximization algorithms rarely return an optimal
partition or anything similar. We propose a specialized algorithm, Bayan, which
returns partitions with a guarantee of either optimality or proximity to an
optimal partition. At the core of the Bayan algorithm is a branch-and-cut
scheme that solves an integer programming formulation of the problem to
optimality or approximate it within a factor. We demonstrate Bayan's
distinctive accuracy and stability over 21 other algorithms in retrieving
ground-truth communities in synthetic benchmarks and node labels in real
networks. Bayan is several times faster than open-source and commercial solvers
for modularity maximization making it capable of finding optimal partitions for
instances that cannot be optimized by any other existing method. Overall, our
assessments point to Bayan as a suitable choice for exact maximization of
modularity in networks with up to 3000 edges (in their largest connected
component) and approximating maximum modularity in larger networks on ordinary
computers.Comment: 6 pages, 2 figures, 1 tabl