A natural way of representing the coauthorship of authors is to use a generalization of graphs known as hypergraphs. A random geometric hypergraph model is proposed here to model coauthorship networks, which is generated by placing nodes on a region of Euclidean space randomly and uniformly, and connecting some nodes if the nodes satisfy particular geometric conditions. Two kinds of geometric conditions are designed to model the collaboration patterns of academic authorities and basic researches respectively. The conditions give geometric expressions of two causes of coauthorship: the authority and similarity of authors. By simulation and calculus, we show that the forepart of the degree distribution of the network generated by the model is mixture Poissonian, and the tail is power-law, which are similar to these of some coauthorship networks. Further, we show more similarities between the generated network and real coauthorship networks: the distribution of cardinalities of hyperedges, high clustering coefficient, assortativity, and small-world propertyA natural way of representing the coauthorship of authors is to use a generalization of graphs known as hypergraphs. A random geometric hypergraph model is proposed here to model coauthorship networks, which is generated by placing nodes on a region of Euclidean space randomly and uniformly, and connecting some nodes if the nodes satisfy particular geometric conditions. Two kinds of geometric conditions are designed to model the collaboration patterns of academic authorities and basic researches respectively. The conditions give geometric expressions of two causes of coauthorship: the authority and similarity of authors. By simulation and calculus, we show that the forepart of the degree distribution of the network generated by the model is mixture Poissonian, and the tail is power-law, which are similar to these of some coauthorship networks. Further, we show more similarities between the generated network and real coauthorship networks: the distribution of cardinalities of hyperedges, high clustering co...
[1]
M. Newman,et al.
The structure of scientific collaboration networks.
,
2000,
Proceedings of the National Academy of Sciences of the United States of America.
[2]
Robert A. Nowlan.
The Man Who Loved Only Numbers
,
2017
.
[3]
Marián Boguñá,et al.
Popularity versus similarity in growing networks
,
2011,
Nature.
[4]
J. A. Rodríguez-Velázquez,et al.
Subgraph centrality and clustering in complex hyper-networks
,
2006
.
[5]
Matjaz Perc,et al.
Growth and structure of Slovenia's scientific collaboration network
,
2010,
J. Informetrics.
[6]
A. Barabasi,et al.
Evolution of the social network of scientific collaborations
,
2001,
cond-mat/0104162.
[7]
Dongyun Yi,et al.
Modeling the Citation Network by Network Cosmology
,
2015,
PloS one.
[8]
Tao Zhou,et al.
MODELLING COLLABORATION NETWORKS BASED ON NONLINEAR PREFERENTIAL ATTACHMENT
,
2007
.
[9]
Mark E. J. Newman,et al.
Power-Law Distributions in Empirical Data
,
2007,
SIAM Rev..
[10]
M. Tomassini,et al.
Empirical analysis of the evolution of a scientific collaboration network
,
2007
.
[11]
J. Moody.
The Structure of a Social Science Collaboration Network: Disciplinary Cohesion from 1963 to 1999
,
2004
.
[12]
Luka Kronegger,et al.
Co-authorship trends and collaboration patterns in the Slovenian sociological community
,
2010
.
[13]
Jianping Li,et al.
Modeling the coevolution between citations and coauthorship of scientific papers
,
2016,
Scientometrics.
[14]
Jianping Li,et al.
A geometric graph model for coauthorship networks
,
2016,
J. Informetrics.
[15]
Loet Leydesdorff,et al.
Network Structure, Self-Organization and the Growth of International Collaboration in Science.Research Policy, 34(10), 2005, 1608-1618.
,
2005,
0911.4299.