Heavy-tailed configuration models at criticality

被引:15
|
作者
Dhara, Souvik [1 ,2 ]
van der Hofstad, Remco [3 ]
van Leeuwaarden, Johan S. H. [3 ,4 ]
Sen, Sanchayan [5 ]
机构
[1] MIT, Dept Math, Cambridge, MA 02139 USA
[2] Microsoft Res New England, Cambridge, MA 02142 USA
[3] Eindhoven Univ Technol, Dept Math & Comp Sci, Eindhoven, Netherlands
[4] Tilburg Univ, Dept Econometr & Operat Res, Tilburg, Netherlands
[5] Indian Inst Sci, Dept Math, Bengaluru, India
基金
欧洲研究理事会; 英国工程与自然科学研究理事会;
关键词
Critical configuration model; Heavy-tailed degree; Thinned Levy process; Augmented multiplicative coalescent; Universality; Critical percolation; RANDOM GRAPHS; MULTIPLICATIVE COALESCENT; SCALING LIMITS; PERCOLATION; COMPONENT; EMERGENCE;
D O I
10.1214/19-AIHP980
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
We study the critical behavior of the component sizes for the configuration model when the tail of the degree distribution of a randomly chosen vertex is a regularly-varying function with exponent tau - 1, where tau is an element of (3, 4). The component sizes are shown to be of the order n((tau -2)/(tau -1)) L(n)(-1) for some slowly-varying function L(.). We show that the re-scaled ordered component sizes converge in distribution to the ordered excursions of a thinned Levy process. This proves that the scaling limits for the component sizes for these heavy-tailed configuration models are in a different universality class compared to the Erdos-Renyi random graphs. Also the joint re-scaled vector of ordered component sizes and their surplus edges is shown to have a distributional limit under a strong topology. Our proof resolves a conjecture by Joseph (Ann. Appl. Probab. 24 (2014) 2560-2594) about the scaling limits of uniform simple graphs with i.i.d. degrees in the critical window, and sheds light on the relation between the scaling limits obtained by Joseph and in this paper, which appear to be quite different. Further, we use percolation to study the evolution of the component sizes and the surplus edges within the critical scaling window, which is shown to converge in finite dimension to the augmented multiplicative coalescent process introduced by Bhamidi et al. (Probab. Theory Related Fields 160 (2014) 733-796). The main results of this paper are proved under rather general assumptions on the vertex degrees. We also discuss how these assumptions are satisfied by some of the frameworks that have been studied previously.
引用
收藏
页码:1515 / 1558
页数:44
相关论文
共 50 条
  • [1] Extended Anderson Criticality in Heavy-Tailed Neural Networks
    Wardak, Asem
    Gong, Pulin
    PHYSICAL REVIEW LETTERS, 2022, 129 (04)
  • [2] CAUSAL DISCOVERY IN HEAVY-TAILED MODELS
    Gnecco, Nicola
    Meinshausen, Nicolai
    Peters, Jonas
    Engelke, Sebastian
    ANNALS OF STATISTICS, 2021, 49 (03): : 1755 - 1778
  • [3] Graphical Models in Heavy-Tailed Markets
    Cardoso, Jose Vinicius de M.
    Ying, Jiaxi
    Palomar, Daniel P.
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 34 (NEURIPS 2021), 2021, 34
  • [4] Global lower mass-bound for critical configuration models in the heavy-tailed regime
    Bhamidi, Shankar
    Dhara, Souvik
    van der Hofstad, Remco
    Sen, Sanchayan
    ELECTRONIC JOURNAL OF PROBABILITY, 2022, 27
  • [5] Large deviations for heavy-tailed factor models
    Svensson, Jens
    Djehiche, Boualem
    STATISTICS & PROBABILITY LETTERS, 2009, 79 (03) : 304 - 311
  • [6] Nonlinear autoregressive models with heavy-tailed innovation
    JIN Yang & AN Hongzhi School of Statistics
    Academy of Mathematics and Systems Science
    Science China Mathematics, 2005, (03) : 333 - 340
  • [7] On stochastic models of teletraffic with heavy-tailed distributions
    Aksenova K.A.
    Journal of Mathematical Sciences, 2011, 176 (2) : 103 - 111
  • [8] Nonlinear autoregressive models with heavy-tailed innovation
    Jin, Y
    An, HZ
    SCIENCE IN CHINA SERIES A-MATHEMATICS, 2005, 48 (03): : 333 - 340
  • [9] Nonlinear autoregressive models with heavy-tailed innovation
    Yang Jin
    Hongzhi An
    Science in China Series A: Mathematics, 2005, 48 (3): : 333 - 340
  • [10] Inference in heavy-tailed vector error correction models
    She, Rui
    Ling, Shiqing
    JOURNAL OF ECONOMETRICS, 2020, 214 (2-3) : 433 - 450