The agglomeration phenomenon influence on the scaling law of the scientific collaboration system

Abstract This paper presents a scientific collaboration hypernetwork evolution model with adjustable clustering coefficient in which the authors are regarded as nodes and the hyperedges are the cooperative articles. Firstly, we build the scientific cooperation hypernetwork through the real paper data which comes from the database of arxive. The empirical results show that the node's hyperdegree follows the power law distribution, but the hyperedge's node-degree has the exponent distribution. In addition, we establish the scientific collaboration clustering hypernetwork evolution model by employing the Poisson process theory and the continuous method for studying the agglomeration phenomenon of scientific cooperation system. The theoretical analysis shows that the node's hyperdegree distribution has scale-free characteristics. The power index of our model is independent of the clustering coefficient, and the theoretical analyses agree with the conducted numerical simulations. Moreover, our model not only describes the scale-free property, but also depicts the phenomenon of agglomeration. Both the properties are usually coexist in the scientific cooperation, our model is more actual.

[1]  A. Nagurney,et al.  A supply chain network equilibrium model , 2002 .

[2]  J. Forrest,et al.  The Evolution of Hyperedge Cardinalities and Bose-Einstein Condensation in Hypernetworks , 2015, Scientific reports.

[3]  Zhen Jin,et al.  Structure of growing complex networks coupling with the friendship and contact relations , 2017 .

[4]  Camellia Sarkar,et al.  Randomness and Structure in Collaboration Networks: A Random Matrix Analysis , 2016, IEEE Transactions on Computational Social Systems.

[5]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[6]  Jin Zhang,et al.  An Improved Archaeology Algorithm Based on Integrated Multi-Source Biological Information for Yeast Protein Interaction Network , 2017, IEEE Access.

[7]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[8]  M. Newman,et al.  Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  Luis Cadarso,et al.  Improved rapid transit network design model: considering transfer effects , 2017, Ann. Oper. Res..

[10]  Qiang Guo,et al.  Improved knowledge diffusion model based on the collaboration hypernetwork , 2015 .

[11]  M. Newman.  Coauthorship networks and patterns of scientific collaboration , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Feng Chen,et al.  The influence of passenger flow on the topology characteristics of urban rail transit networks , 2017 .

[13]  Mohammed Shahadat Uddin,et al.  Evolutionary dynamics of scientific collaboration networks: multi-levels and cross-time analysis , 2011, Scientometrics.

[14]  Tao Zhou,et al.  MODELLING COLLABORATION NETWORKS BASED ON NONLINEAR PREFERENTIAL ATTACHMENT , 2007 .

[15]  Jian-Wei Wang,et al.  Evolving hypernetwork model , 2010 .

[16]  M E Newman,et al.  Scientific collaboration networks. I. Network construction and fundamental results. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  Qi Suo,et al.  Information spreading dynamics in hypernetworks , 2018 .

[18]  Zhao-Long Hu,et al.  Knowledge diffusion in the collaboration hypernetwork , 2015 .

[19]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  D. Zanette,et al.  Potential-partnership networks and the dynamical structure of monogamous populations , 2010 .

[21]  Matheus Palhares Viana,et al.  On time-varying collaboration networks , 2013, J. Informetrics.

[22]  He Da-ren.  An Empirical Statistical Investigation on Chinese Mainland Movie Network , 2007 .

[23]  Jin-Li Guo,et al.  Study of the variable growth hypernetworks influence on the scaling law , 2017 .

[24]  Tao Zhou,et al.  Evolving model of weighted networks inspired by scientific collaboration networks , 2005 .

[25]  Bart Baesens,et al.  Predicting time-to-churn of prepaid mobile telephone customers using social network analysis , 2016, J. Oper. Res. Soc..

[26]  Jing Zhao,et al.  Network evolution model for supply chain with manufactures as the core , 2018, PloS one.

[27]  Beom Jun Kim,et al.  Growing scale-free networks with tunable clustering. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  B. Bollobás.  The evolution of random graphs , 1984 .

[29]  S. N. Dorogovtsev,et al.  Self-organization of collaboration networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[30]  P. Ghauri,et al.  Network view of MNCs' socio-political behavior , 2008 .

[31]  Jin-Li Guo,et al.  Brand effect versus competitiveness in hypernetworks , 2015, Chaos.

[32]  A. Barabasi,et al.  Evolution of the social network of scientific collaborations , 2001, cond-mat/0104162.

[33]  Xin-Yun Zhu,et al.  Non-uniform Evolving Hypergraphs and Weighted Evolving Hypergraphs , 2016, Scientific Reports.

[34]  Huijie Yang,et al.  Multifractals embedded in short time series: An unbiased estimation of probability moment. , 2016, Physical review. E.

[35]  M. Newman,et al.  Scaling and percolation in the small-world network model. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.