297 research outputs found
Exploring cooperative game mechanisms of scientific coauthorship networks
Scientific coauthorship, generated by collaborations and competitions among
researchers, reflects effective organizations of human resources. Researchers,
their expected benefits through collaborations, and their cooperative costs
constitute the elements of a game. Hence we propose a cooperative game model to
explore the evolution mechanisms of scientific coauthorship networks. The model
generates geometric hypergraphs, where the costs are modelled by space
distances, and the benefits are expressed by node reputations, i. e. geometric
zones that depend on node position in space and time. Modelled cooperative
strategies conditioned on positive benefit-minus-cost reflect the spatial
reciprocity principle in collaborations, and generate high clustering and
degree assortativity, two typical features of coauthorship networks. Modelled
reputations generate the generalized Poisson parts and fat tails appeared in
specific distributions of empirical data, e. g. paper team size distribution.
The combined effect of modelled costs and reputations reproduces the
transitions emerged in degree distribution, in the correlation between degree
and local clustering coefficient, etc. The model provides an example of how
individual strategies induce network complexity, as well as an application of
game theory to social affiliation networks
Hamiltonian System Approach to Distributed Spectral Decomposition in Networks
Because of the significant increase in size and complexity of the networks,
the distributed computation of eigenvalues and eigenvectors of graph matrices
has become very challenging and yet it remains as important as before. In this
paper we develop efficient distributed algorithms to detect, with higher
resolution, closely situated eigenvalues and corresponding eigenvectors of
symmetric graph matrices. We model the system of graph spectral computation as
physical systems with Lagrangian and Hamiltonian dynamics. The spectrum of
Laplacian matrix, in particular, is framed as a classical spring-mass system
with Lagrangian dynamics. The spectrum of any general symmetric graph matrix
turns out to have a simple connection with quantum systems and it can be thus
formulated as a solution to a Schr\"odinger-type differential equation. Taking
into account the higher resolution requirement in the spectrum computation and
the related stability issues in the numerical solution of the underlying
differential equation, we propose the application of symplectic integrators to
the calculation of eigenspectrum. The effectiveness of the proposed techniques
is demonstrated with numerical simulations on real-world networks of different
sizes and complexities
Feature analysis of multidisciplinary scientific collaboration patterns based on PNAS
The features of collaboration patterns are often considered to be different
from discipline to discipline. Meanwhile, collaborating among disciplines is an
obvious feature emerged in modern scientific research, which incubates several
interdisciplines. The features of collaborations in and among the disciplines
of biological, physical and social sciences are analyzed based on 52,803 papers
published in a multidisciplinary journal PNAS during 1999 to 2013. From those
data, we found similar transitivity and assortativity of collaboration patterns
as well as the identical distribution type of collaborators per author and that
of papers per author, namely a mixture of generalized Poisson and power-law
distributions. In addition, we found that interdisciplinary research is
undertaken by a considerable fraction of authors, not just those with many
collaborators or those with many papers. This case study provides a window for
understanding aspects of multidisciplinary and interdisciplinary collaboration
patterns
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