The impact of the marginal utility behavior on single-layer networks with limited contact

被引:0
|
作者
Ju, Xiangyu [1 ]
Liu, Siyuan [2 ]
Zhu, Xuzhen [2 ]
机构
[1] Beijing Univ Posts & Telecommun, Sch Comp Sci, Nat Pilot Software Engn Sch, Beijing 100876, Peoples R China
[2] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China
来源
关键词
Complex network; information propagation; limited contact; threshold model; marginal utility behavior;
D O I
10.1142/S0129183124501432
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The information propagation on the social network has been an important research topic, with a focus on the significant influence of individual behaviors. The marginal utility behavior can affect the process of information propagation. But most previous research ignore it. Besides, the process can also be influenced by limited-contact capacity, which increase the complexity of networks. In this paper, the marginal utility behavior model on the single-layer network with limited-contact capacity is proposed first. Then the edge-based compartmental (EBC) method is used to explore the novel information propagation mechanism. Through experiments, it was found that when individuals show an increasing marginal utility behavior, with the propagation probability increasing, the final spreading scope shows a discontinuous increase pattern by weakening behavior. However, the final spreading scope shows no outbreak by strengthening behavior. In contrast, when individuals show a diminishing marginal utility behavior, with the propagation probability increasing, the final spreading scope shows a continuous increase pattern by strengthening. Nevertheless, the final spreading scope shows a discontinuous increasing by weakening. What's more, the limited-contact capacity and the degree distribution heterogeneity can also change the information propagation pattern. Besides, the experimental results are in agreement with the theoretical results.
引用
收藏
页数:18
相关论文
共 50 条
  • [31] Serving Multicast Requests on Single-Layer and Multilayer Flexgrid Networks
    Ruiz, Marc
    Velasco, Luis
    JOURNAL OF OPTICAL COMMUNICATIONS AND NETWORKING, 2015, 7 (03) : 146 - 155
  • [32] Modulated surface of single-layer graphene controls cell behavior
    Kalbacova, Marie Hubalek
    Verdanova, Martina
    Broz, Antonin
    Vetushka, Aliaksei
    Fejfar, Antonin
    Kalbac, Martin
    CARBON, 2014, 72 : 207 - 214
  • [33] Single-Layer Unit Cells with Optimized Phase Angle Behavior
    Dieter, S.
    Fischer, C.
    Menzel, W.
    2009 3RD EUROPEAN CONFERENCE ON ANTENNAS AND PROPAGATION, VOLS 1-6, 2009, : 1094 - 1098
  • [34] Phonon-Limited Electron Mobility in Single-Layer MoS2
    Zeng Lang
    Xin Zheng
    Chen Shao-Wen
    Du Gang
    Kang Jin-Feng
    Liu Xiao-Yan
    CHINESE PHYSICS LETTERS, 2014, 31 (02)
  • [35] Single-Layer Behavior and Its Breakdown in Twisted Graphene Layers
    Luican, A.
    Li, Guohong
    Reina, A.
    Kong, J.
    Nair, R. R.
    Novoselov, K. S.
    Geim, A. K.
    Andrei, E. Y.
    PHYSICAL REVIEW LETTERS, 2011, 106 (12)
  • [36] A new Jacobian matrix for optimal learning of single-layer neural networks
    Peng, Jian-Xun
    Li, Kang
    Irwin, George W.
    IEEE TRANSACTIONS ON NEURAL NETWORKS, 2008, 19 (01): : 119 - 129
  • [37] Supervised Learning of Single-Layer Spiking Neural Networks for Image Classification
    Ma, Qiang
    Lin, Xianghong
    Wang, Xiangwen
    2018 2ND INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE APPLICATIONS AND TECHNOLOGIES (AIAAT 2018), 2018, 435
  • [38] Biologically plausible single-layer networks for nonnegative independent component analysis
    Lipshutz, David
    Pehlevan, Cengiz
    Chklovskii, Dmitri B.
    BIOLOGICAL CYBERNETICS, 2022, 116 (5-6) : 557 - 568
  • [39] Face identification with second-order pooling in single-layer networks
    Shen, Fumin
    Yang, Yang
    Zhou, Xiang
    Liu, Xianglong
    Shao, Jie
    NEUROCOMPUTING, 2016, 187 : 11 - 18
  • [40] An Incremental Optimal Weight Learning Machine of Single-Layer Neural Networks
    Ke, Hai-Feng
    Lu, Cheng-Bo
    Li, Xiao-Bo
    Zhang, Gao-Yan
    Mei, Ying
    Shen, Xue-Wen
    SCIENTIFIC PROGRAMMING, 2018, 2018