MATHEMATICAL MODELLING OF THE NETWORK OF PROFESSIONAL INTERACTIONS

被引:1
作者
Pitsik, E. N. [1 ]
Goremyko, M. V. [1 ]
Makarov, V. V. [1 ]
Hramov, A. E. [1 ]
机构
[1] Yuri Gagarin State Tech Univ Saratov, 77,Politech Skaya St, Saratov 410054, Russia
来源
IZVESTIYA VYSSHIKH UCHEBNYKH ZAVEDENIY-PRIKLADNAYA NELINEYNAYA DINAMIKA | 2018年 / 26卷 / 01期
关键词
complex network; multiplex network; mathematical modelling; social system;
D O I
10.18500/0869-6632-2018-26-1-21-32
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Description of real-world systems of interacting units by the means of network model is an effective method of research both in macro- and microscale. In addition, using the simple one-layer networks with one type of connections between the nodes when describing real-world networks is inefficiently because of their complex structural and dynamical nature. Besides, presence of similar features in real networks that are fundamentally different by their nature provided a wide spread of proposed model in many fields of science for the acquisition of new fundamental knowledge about functioning of the real network structures. For this reason the object of this article is modelling of multiplex network build on the basis of real data about professional interactions in world-wide musical community. The changes in characteristics in in proposed model reflects structural and dynamical features of real network, such as scale-free connection structure and clusters formation. Results obtained for multiplex network shows that after uniting the isolated systems their topologies undergo noticeable changes. In particular, significant changes in centrality values and in cluster formation inside the network were obtained. Besides, the correlations between the characteristics and dynamics of these correlations in process of uniting the isolated systems in general network. Obtained results confirm the effectiveness of multiplex network model for studying structural and dynamical processes of many real systems.
引用
收藏
页码:21 / 32
页数:12
相关论文
共 30 条
  • [1] Aggarwal CC, 2011, SOCIAL NETWORK DATA ANALYTICS, P1
  • [2] Arnaboldi V, 2013, IEEE CONF COMPUT, P229
  • [3] Structural measures for multiplex networks
    Battiston, Federico
    Nicosia, Vincenzo
    Latora, Vito
    [J]. PHYSICAL REVIEW E, 2014, 89 (03):
  • [4] A faster algorithm for betweenness centrality
    Brandes, U
    [J]. JOURNAL OF MATHEMATICAL SOCIOLOGY, 2001, 25 (02) : 163 - 177
  • [5] Epidemics in Partially Overlapped Multiplex Networks
    Buono, Camila
    Alvarez-Zuzek, Lucila G.
    Macri, Pablo A.
    Braunstein, Lidia A.
    [J]. PLOS ONE, 2014, 9 (03):
  • [6] Network robustness and fragility: Percolation on random graphs
    Callaway, DS
    Newman, MEJ
    Strogatz, SH
    Watts, DJ
    [J]. PHYSICAL REVIEW LETTERS, 2000, 85 (25) : 5468 - 5471
  • [7] Dawson S., 2014, CURRENT STATE FUTURE, P231
  • [8] Dunbar's Number: Group Size and Brain Physiology in Humans Reexamined
    de Ruiter, Jan
    Weston, Gavin
    Lyon, Stephen M.
    [J]. AMERICAN ANTHROPOLOGIST, 2011, 113 (04) : 557 - 568
  • [9] Prediction of emerging technologies based on analysis of the US patent citation network
    Erdi, Peter
    Makovi, Kinga
    Somogyvari, Zoltan
    Strandburg, Katherine
    Tobochnik, Jan
    Volf, Peter
    Zalanyi, Laszlo
    [J]. SCIENTOMETRICS, 2013, 95 (01) : 225 - 242
  • [10] Modeling Users' Activity on Twitter Networks: Validation of Dunbar's Number
    Goncalves, Bruno
    Perra, Nicola
    Vespignani, Alessandro
    [J]. PLOS ONE, 2011, 6 (08):