中文版 | English
题名

复杂网络上的间接渗流与相变

其他题名
INDIRECT PERCOLATION AND PHASE TRANSITION IN THE COMPLEX NETWORKS
姓名
姓名拼音
CHEN Shuhuan
学号
12132896
学位类型
硕士
学位专业
0701 数学
学科门类/专业学位类别
07 理学
导师
胡延庆
导师单位
统计与数据科学系
论文答辩日期
2023-04-28
论文提交日期
2023-06-29
学位授予单位
南方科技大学
学位授予地点
深圳
摘要

复杂网络是一种描述自然和社会系统各种结构和功能的模型,现如今它已经成为涵盖数学,物理,生物,计算机,社会科学等多个领域的交叉方向。渗流理论起源于统计物理,几十年来数学家和物理学家对网格上的渗流现象做了大量研究。随着20世纪末网络科学的兴起,渗流理论被广泛运用于复杂网络研究,诸如网络鲁棒性,疾病传播,演化博弈等问题,现已成为研究复杂网络的一种重要手段。长期以来,学者们提出很多种渗流模型来模拟现实世界中可能的各种网络演化和动力学过程,然而绝大多数渗流模型仅仅考虑顶点自身或者顶点邻居的直接作用。事实上,来自自身和邻居以外的间接作用对网络的影响不容忽视,而对网络上间接作用的研究尚且不多。

本文研究了一般的间接渗流模型,考虑了顶点初始时随机活跃的比例,从网络的度分布出发,分别在有向网络和无向网络上,建立了间接渗流的自洽方程,给出了求解网络上最大活跃连通分支的规模的公式,并且在ER随机网络上进行模拟,得到了与理论的数值解相一致的模拟结果,进一步还分析了间接渗流的相变情况,发现随着控制参数的变化,相变会在连续相变,混合相变,跳跃相变三种情况中变化,而且在有向和无向网络中,间接渗流体现出几乎相同的特征。

关键词
语种
中文
培养类别
独立培养
入学年份
2021
学位授予年份
2023-06
参考文献列表

[1] AHN Y Y, HAN S, KWAK H, et al. Analysis of topological characteristics of huge online socialnetworking services[J]. ACM, 2007.
[2] BARABáSI A, JEONG H, NéDA Z, et al. Evolution of the social network of scientific collab￾orations[J]. Physica A-statistical Mechanics and Its Applications, 2002, 311(3): 590-614.
[3] NEWMAN M E J. Scientific collaboration networks. I. Network construction and fundamentalresults[J]. Phys. Rev. E, 2001, 64: 016131.
[4] ERDOS P, RéNYI A. On random graphs[J]. Publications Mathematicae, 1959, 6: 290-297.
[5] ERDOS P, RéNYI A. On the strength of connectedness of random graphs[J]. Acta MathematicaScientia Hungary, 1961.
[6] ERDOS P, RéNYI A. On the evolution of random graphs[J]. Publ.Mah.Inst.Hung.Acad.Sci,1960.
[7] WATTS D J, STROGATZ S H. Collective dynamics of ’small-world’ networks.[J]. Nature,1998.
[8] BARABASI A L, ALBERT R. Emergence of Scaling in Random Networks.[J]. Science, 1999.
[9] NEWMAN M. The spread of epidemic disease on networks[J]. Physical Review E StatisticalNonlinear & Soft Matter Physics, 2002, 66(1 Pt 2): 016128.
[10] RAMANI A, CARSTEA A S, WILLOX R, et al. Oscillating epidemics: a discrete-time model[J]. Physica A: Statistical Mechanics and its Applications, 2004, 333(none): 278-292.
[11] ZANETTE D H. Dynamics of rumor propagation on small-world networks[J]. Physical review.E, Statistical, nonlinear, and soft matter physics, 2002, 65: 041908-041908.
[12] PAGE L, BRIN S, MOTWANI R, et al. The PageRank Citation Ranking: Bringing Order to theWeb[J]. Stanford Digital Libraries Working Paper, 1998.
[13] CALLAWAY D S, NEWMAN M, STROGATZ S H, et al. Network robustness and fragility:Percolation on random graphs[J]. Physical Review Letters, 2000, 85(25): 5468.
[14] NEWMAN M, STROGATZ S H, WATTS D J. Random graphs with arbitrary degree distribu￾tions and their applications[J]. Physical Review E Statistical Nonlinear & Soft Matter Physics,2001, 64.
[15] CASTELLANO C, FORTUNATO S, LORETO V. Statistical physics of social dynamics[J].Review of Modern Physics, 2007, 81(2).
[16] DOROGOVTSEV S N, GOLTSEV A V, MENDES J. k-core organization of complex networks[J]. Phys.rev.lett, 2006, 96(4): 185-194.
[17] BAXTER G J, DOROGOVTSEV S N, GOLTSEV A V, et al. Bootstrap percolation on complexnetworks[J]. Physical Review E, 2010, 82(1): 011103.
[18] GRANOVETTER, MARK. Threshold Models of Collective Behavior[J]. American Journal ofSociology, 1978, 83(6): 1420-1443.40参考文献
[19] MORONE F, MAKSE H A. Influence maximization in complex networks through optimalpercolation[J]. Nature (London), 2015, 524(7563): 65-68.
[20] ACHLIOPTAS D, D’SOUZA R M, SPENCER J. Explosive Percolation in Random Networks[J]. Science (American Association for the Advancement of Science), 2009, 323(5920): 1453-1455.
[21] STUNKARD A J, SORENSEN T I, HANIS C, et al. The Spread of Obesity in a Large SocialNetwork over 32 Years[J]. N Engl J Med, 2007, 357(4): 370-379.
[22] ROSENQUIST, NIELS J. The Spread of Alcohol Consumption Behavior in a Large SocialNetwork[J]. Annals of Internal Medicine, 2010, 152(7): 426.
[23] FOWLER J H, CHRISTAKIS N A. Cooperative behavior cascades in human social networks[J]. Proceedings of the National Academy of Sciences of the United States of America, 2010,107(12): 5334-5338.
[24] RUDOLPH A E, CRAWFORD N D, LATKIN C, et al. Individual and neighborhood correlatesof membership in drug using networks with a higher prevalence of HIV in New York City (2006–2009)[J]. Annals of Epidemiology, 2013, 23(5): 267-274.
[25] XIE J, WANG X, FENG L, et al. Indirect influence in social networks as an induced percolationphenomenon[J]. Proceedings of the National Academy of Sciences of the United States ofAmerica., 2022(9): 119.
[26] BARTHELEMY M, AMARAL L. Small-World Networks: Evidence for a Crossover Picture[J]. Physical Review Letters, 1999, 82(15).
[27] BARRAT A, WEIGT M. On the properties of small-world network models[J]. European Phys￾ical Journal B (Condensed Matter and Complex Systems), 2000, 13(3): 547-560.
[28] CRISTIAN, F., MOUKARZEL. Spreading and shortest paths in systems with sparse long-rangeconnections[J]. Physical Review E, 1999, 60(6): R6263–R6266.
[29] BARABáSI A. Mean-field theory for scale-free[J]. Physica A: Statistical Mechanics and itsApplications, 1999, 2(72): 172-182.
[30] BROADBENT S, HAMMERSLEY J. Percolation processes. I: Crystals and mazes[J]. Mathe￾matical Proceedings of the Cambridge Philosophical Society, 1957, 53.
[31] LIU Y Y, CSOKA E, ZHOU H, et al. Core Percolation on Complex Networks[J]. Physicalreview letters, 2012, 109(20): 205703.1-205703.5.
[32] BAUER M, GOLINELLI O. Core percolation in random graphs: a critical phenomena analysis[J]. The European physical journal. B, Condensed matter physics, 2001, 24(3): 339-352.
[33] CHALUPA J, LEATH P L, REICH G R. Bootstrap percolation on a Bethe lattice[J]. Journal ofPhysics C: Solid State Physics, 1979, 12(1): L31.
[34] DI MURO M A, VALDEZ L D, STANLEY H E, et al. Insights into bootstrap percolation: Itsequivalence with k-core percolation and the giant component[J]. Physical review. E, 2019, 99(2-1): 022311-022311.
[35] DA COSTA R A, DOROGOVTSEV S N, GOLTSEV A V, et al. Explosive Percolation Transi￾tion is Actually Continuous[J/OL]. Phys. Rev. Lett., 2010, 105: 255701. https://link.aps.org/doi/10.1103/PhysRevLett.105.255701.41参考文献
[36] RIORDAN O, WARNKE L. Explosive Percolation Is Continuous[J]. Science (American As sociation for the Advancement of Science), 2011, 333(6040): 322-324.
[37] PICCARDI C, CASAGRANDI R. Inefficient epidemic spreading in scale-free networks[J].Physical Review E, 2008, 77.
[38] JONSSON P F, CAVANNA T, ZICHA D, et al. Cluster analysis of networks generated throughhomology: automatic identification of important protein communities involved in cancer metas tasis[J]. BMC bioinformatics, 2006, 7(1): 2-2.
[39] GONZáLEZ M, HERRMANN H, KERTéSZ J, et al. Community structure and ethnic prefer ences in school friendship networks[J]. Physica A, 2007, 379(1): 307-316.
[40] PALLA G, DERANYI I, FARKAS I, et al. Uncovering the overlapping community structure ofcomplex networks in nature and society[J]. Nature, 2005, 435(7043): 814.
[41] BATES J P A. Global topological features of cancer proteins in the human interactome[J].Bioinformatics, 2006, 22(18): 2291-2297.
[42] LI D, FU B, WANG Y, et al. Percolation transition in dynamical traffic network with evolvingcritical bottlenecks[J]. Proceedings of the National Academy of Sciences, 2015, 112(3): 669-672.
[43] BIHAM O, MIDDLETON A A. Self-organization and a dynamical transition in traffic-flowmodels[J]. Physical review. A, Atomic, molecular, and optical physics, 1992, 46(10): R6124-R6127.
[44] HAMEDMOGHADAM H, JALILI M, VU H L, et al. Percolation of heterogeneous flows un covers the bottlenecks of infrastructure networks[J]. Nature Communications, 2021, 12.
[45] YANG Y, WANG J, MOTTER A E. Network observability transitions[J]. Physical reviewletters, 2013, 109(25): 258701-258701.
[46] YANG Y, RADICCHI F. Observability transition in real networks[J]. Physical review. E, 2016,94(3-1): 030301-030301.
[47] SZABó G, TOKE C. Evolutionary prisoner’s dilemma game on a square lattice[J]. PhysicalReview E, 1998, 58(1): 69-73.
[48] SZABO G, FATH G. Evolutionary games on graphs[J]. Physics reports, 2007, 446(4): 97-216.
[49] NEWMAN M E J M E J. Monte Carlo methods in statistical physics[M]. Oxford: ClarendonPress, 1999.
[50] GUIMARãES P R, Jr, PIRES M M, JORDANO P, et al. Indirect effects drive coevolution inmutualistic networks[J]. Nature (London), 2017, 550(7677): 511-514.
[51] 尼古拉斯·克里斯塔基斯, 詹姆斯·富. 大连接[M]. 中国人民大学出版社, 2013.

所在学位评定分委会
数学
国内图书分类号
O157.5
来源库
人工提交
成果类型学位论文
条目标识符http://sustech.caswiz.com/handle/2SGJ60CL/544648
专题理学院_统计与数据科学系
推荐引用方式
GB/T 7714
陈枢桓. 复杂网络上的间接渗流与相变[D]. 深圳. 南方科技大学,2023.
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可 操作
12132896-陈枢桓-统计与数据科学(1972KB)----限制开放--请求全文
个性服务
原文链接
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
导出为Excel格式
导出为Csv格式
Altmetrics Score
谷歌学术
谷歌学术中相似的文章
[陈枢桓]的文章
百度学术
百度学术中相似的文章
[陈枢桓]的文章
必应学术
必应学术中相似的文章
[陈枢桓]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
[发表评论/异议/意见]
暂无评论

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。