• 卓越行动计划二期中文领军期刊
  • 中国科学引文数据库核心期刊
  • 中文核心期刊、中国科技核心期刊
  • 第1、2届国家期刊奖
  • 第3届国家期刊奖百种重点期刊奖
  • 中国精品科技期刊、中国百强报刊
  • 百种中国杰出学术期刊
ZHOU Jianlin, FAN Ying, DI Zengru. Progress in complex networks[J]. Journal of Beijing Normal University(Natural Science), 2023, 59(5): 691-704. DOI: 10.12202/j.0476-0301.2023153
Citation: ZHOU Jianlin, FAN Ying, DI Zengru. Progress in complex networks[J]. Journal of Beijing Normal University(Natural Science), 2023, 59(5): 691-704. DOI: 10.12202/j.0476-0301.2023153

Progress in complex networks

More Information
  • Received Date: April 27, 2023
  • Accepted Date: September 05, 2023
  • Available Online: September 20, 2023
  • This paper reviews the related research work on“complex network modeling and analysis”published by the School of Systems Science, Beijing Normal University since its establishment in 2013. Firstly, according to different network types, we introduce the important achievements in the field of complex networks theory from the aspects of network structure and dynamics analysis. Secondly, we summarize the main research results of the application of complex networks analysis in socioeconomic systems and earth systems. Finally, we put forward some suggestions on the development direction of complex networks research in the School of Systems Science, Beijing Normal University.
  • [1]
    朱凤辉,樊瑛. 基于电阻网络的节点重要性判别[J]. 北京师范大学学报(自然科学版),2013,49(6):636
    [2]
    ZHU F H,WANG W X,DI Z R,et al. Identifying and characterizing key nodes among communities based on electrical-circuit networks[J]. PLoS One,2014,9(6):e97021 doi: 10.1371/journal.pone.0097021
    [3]
    NIU Q K,ZENG A,FAN Y,et al. Robustness of centrality measures against network manipulation[J]. Physica A: Statistical Mechanics and Its Applications,2015,438:124 doi: 10.1016/j.physa.2015.06.031
    [4]
    ZHANG J,LI X T,WANG X R,et al. Scaling behaviours in the growth of networked systems and their geometric origins[J]. Scientific Reports,2015,5:9767 doi: 10.1038/srep09767
    [5]
    ZOU L,WANG C,ZENG A,et al. Link prediction in growing networks with aging[J]. Soc Networks,2021,65:1 doi: 10.1016/j.socnet.2020.11.001
    [6]
    ZOU L,WANG A,ZENG A,et al. Connecting node age with centrality measurement in growing networks[J]. Journal of Statistical Mechanics:Theory and Experiment,2019,2019(7):073403 doi: 10.1088/1742-5468/ab270d
    [7]
    GUO L Z,LOU X D,SHI P T,et al. Flow distances on open flow networks[J]. Physica A:Statistical Mechanics and Its Applications,2015,437:235 doi: 10.1016/j.physa.2015.05.070
    [8]
    ZHANG J A,WU L F. Allometry and dissipation of ecological flow networks[J]. PLoS One,2013,8(9):e72525 doi: 10.1371/journal.pone.0072525
    [9]
    LOU X D,LI Y,GU W W,et al. The atlas of Chinese world wide web ecosystem shaped by the collective attention flows[J]. PLoS One,2016,11(11):e0165240 doi: 10.1371/journal.pone.0165240
    [10]
    WU Z N,DI Z R,FAN Y. An asymmetric popularity-similarity optimization method for embedding directed networks into hyperbolic space[J]. Complexity,2020,2020:1
    [11]
    XIN R Y,ZHANG J,SHAO Y T. Complex network classification with convolutional neural network[J]. Tsinghua Science and Technology,2020,25(4):447 doi: 10.26599/TST.2019.9010055
    [12]
    GU W W,GONG L,LOU X D,et al. The hidden flow structure and metric space of network embedding algorithms based on random walks[J]. Scientific Reports,2017,7:13114 doi: 10.1038/s41598-017-12586-y
    [13]
    JIANG Y X,LI M,FAN Y,et al. Characterizing dissimilarity of weighted networks[J]. Scientific Reports,2021,11:5768 doi: 10.1038/s41598-021-85175-9
    [14]
    JIANG Y X,LI M,DI Z R. Dissimilarity-based filtering and compression of complex weighted networks[J]. Europhysics Letters,2022,139(4):42003 doi: 10.1209/0295-5075/ac8286
    [15]
    CHEN W T,ZENG A,CUI X H. Preserving the topological properties of complex networks in network sampling[J]. Chaos,2022,32(3):033122 doi: 10.1063/5.0076854
    [16]
    LIN G Q,DI Z R,FAN Y. Cascading failures in complex networks with community structure[J]. International Journal of Modern Physics C,2014,25(5):1440005 doi: 10.1142/S0129183114400051
    [17]
    FENG Y Q,SUN B H,ZENG A. Cascade of links in complex networks[J]. Physics Letters A,2017,381(4):263 doi: 10.1016/j.physleta.2016.11.008
    [18]
    SUN Y,MA L,ZENG A,et al. Spreading to localized targets in complex networks[J]. Scientific Reports,2016,6:38865 doi: 10.1038/srep38865
    [19]
    ZHANG A B,ZENG A,FAN Y,et al. Guiding propagation to localized target nodes in complex networks[J]. Chaos,2021,31(7):073104 doi: 10.1063/5.0029411
    [20]
    LI R Q,WANG W X,DI Z R. Effects of human dynamics on epidemic spreading in Côte d’Ivoire[J]. Physica A:Statistical Mechanics and Its Applications,2017,467:30 doi: 10.1016/j.physa.2016.09.059
    [21]
    WANG N N,WANG Y J,QIU S H,et al. Epidemic spreading with migration in networked metapopulation[J]. Communications in Nonlinear Science and Numerical Simulation,2022,109:106260 doi: 10.1016/j.cnsns.2022.106260
    [22]
    ZHANG J,DONG L,ZHANG Y B,et al. Investigating time,strength,and duration of measures in controlling the spread of COVID-19 using a networked meta-population model[J]. Nonlinear Dynamics,2020,101(3):1789 doi: 10.1007/s11071-020-05769-2
    [23]
    SHEN Z S,CAO S N,WANG W X,et al. Locating the source of diffusion in complex networks by time-reversal backward spreading[J]. Physical Review E,2016,93(3):032301
    [24]
    FU L,SHEN Z S,WANG W X,et al. Multi-source localization on complex networks with limited observers[J]. EPL (Europhysics Letters),2016,113(1):18006 doi: 10.1209/0295-5075/113/18006
    [25]
    HU Z L,HAN X,LAI Y C,et al. Optimal localization of diffusion sources in complex networks[J]. Royal Society Open Science,2017,4(4):170091 doi: 10.1098/rsos.170091
    [26]
    WANG W X,LAI Y C,GREBOGI C. Data based identification and prediction of nonlinear and complex dynamical systems[J]. Physics Reports,2016,644:1 doi: 10.1016/j.physrep.2016.06.004
    [27]
    SHEN Z S,WANG W X,FAN Y,et al. Reconstructing propagation networks with natural diversity and identifying hidden sources[J]. Nature Communications,2014,5:4323 doi: 10.1038/ncomms5323
    [28]
    LI J W,SHEN Z S,WANG W X,et al. Universal data-based method for reconstructing complex networks with binary-state dynamics[J]. Physical Review E,2017,95(3):032303
    [29]
    HAN X A,SHEN Z S,WANG W X,et al. Robust reconstruction of complex networks from sparse data[J]. Physical Review Letters,2015,114(2):028701 doi: 10.1103/PhysRevLett.114.028701
    [30]
    MA L,HAN X,SHEN Z S,et al. Efficient reconstruction of heterogeneous networks from time series via compressed sensing[J]. PLoS One,2015,10(11):e0142837 doi: 10.1371/journal.pone.0142837
    [31]
    HAN X,SHEN Z S,WANG W X,et al. Reconstructing direct and indirect interactions in networked public goods game[J]. Scientific Reports,2016,6:30241 doi: 10.1038/srep30241
    [32]
    TANG S Q,SHEN Z S,WANG W X,et al. Uncovering transportation networks from traffic flux by compressed sensing[J]. European Physical Journal B,2015,88(8):211 doi: 10.1140/epjb/e2015-60234-y
    [33]
    ZHANG Z,ZHAO Y,LIU J,et al. A general deep learning framework for network reconstruction and dynamics learning[J]. Applied Network Science,2019,4(1):1 doi: 10.1007/s41109-018-0108-x
    [34]
    CHEN M Y,ZHANG Y,ZHANG Z,et al. Inferring network structure with unobservable nodes from time series data[J]. Chaos,2022,32(1):013126 doi: 10.1063/5.0076521
    [35]
    ZHANG Y,GUO Y,ZHANG Z,et al. Universal framework for reconstructing complex networks and node dynamics from discrete or continuous dynamics data[J]. Physical Review E,2022,106(3):034315 doi: 10.1103/PhysRevE.106.034315
    [36]
    YUAN Z Z,ZHAO C,DI Z R,et al. Exact controllability of complex networks[J]. Nature Communications,2013,4:2447 doi: 10.1038/ncomms3447
    [37]
    LI J W,YUAN Z Z,FAN Y,et al. Controllability of fractal networks:an analytical approach[J]. EPL (Europhysics Letters),2014,105(5):58001 doi: 10.1209/0295-5075/105/58001
    [38]
    ZHAO C,WANG W X,LIU Y Y,et al. Intrinsic dynamics induce global symmetry in network controllability[J]. Scientific Reports,2015,5:8422 doi: 10.1038/srep08422
    [39]
    GAO X D,WANG W X,LAI Y C. Control efficacy of complex networks[J]. Scientific Reports,2016,6:28037 doi: 10.1038/srep28037
    [40]
    刘雨含,李乐,樊瑛. 股票符号网络研究[J]. 北京师范大学学报(自然科学版),2018,54(2):186
    [41]
    GU K,FAN Y,ZENG A,et al. Analysis on large-scale rating systems based on the signed network[J]. Physica A:Statistical Mechanics and Its Applications,2018,507:99 doi: 10.1016/j.physa.2018.05.048
    [42]
    LI L,GU K,ZENG A,et al. Modeling online social signed networks[J]. Physica A:Statistical Mechanics and Its Applications,2018,495:345 doi: 10.1016/j.physa.2017.12.089
    [43]
    ZHOU J L,LI L B,ZENG A,et al. Random walk on signed networks[J]. Physica A:Statistical Mechanics and Its Applications,2018,508:558 doi: 10.1016/j.physa.2018.05.139
    [44]
    张奥博,樊瑛,狄增如. 符号网络下平衡结构对舆论形成的影响[J]. 复杂系统与复杂性科学,2019,16(3):22
    [45]
    LI L B,FAN Y,ZENG A,et al. Binary opinion dynamics on signed networks based on Ising model[J]. Physica A:Statistical Mechanics and Its Applications,2019,525:433 doi: 10.1016/j.physa.2019.03.011
    [46]
    GAO Y,FAN Y,DI Z R. The dynamics of two-state public opinion propagation on signed networks[J]. Journal of Systems Science and Complexity,2021,34(1):251 doi: 10.1007/s11424-020-9226-5
    [47]
    LI L B,ZENG A,FAN Y,et al. Modeling multi-opinion propagation in complex systems with heterogeneous relationships via Potts model on signed networks[J]. Chaos,2022,32(8):083101 doi: 10.1063/5.0084525
    [48]
    LI A W,XU X K,FAN Y. Immunization strategies for false information spreading on signed social networks[J]. Chaos,Solitons and Fractals,2022,162:112489 doi: 10.1016/j.chaos.2022.112489
    [49]
    ZHONG X W,FAN Y,DI Z R. The evolution of cooperation in public goods games on signed networks[J]. Physica A:Statistical Mechanics and Its Applications,2021,582:126217 doi: 10.1016/j.physa.2021.126217
    [50]
    ZHONG X W,HUANG G,WANG N N,et al. Dynamical analysis of evolutionary public goods game on signed networks[J]. Chaos,2022,32(2):023107 doi: 10.1063/5.0070358
    [51]
    GU K,FAN Y,DI Z R. How to predict recommendation lists that users do not like[J]. Physica A: Statistical Mechanics and Its Applications,2020,537:122684 doi: 10.1016/j.physa.2019.122684
    [52]
    吴宗柠,狄增如,樊瑛. 多层网络的结构与功能研究进展[J]. 电子科技大学学报,2021,50(1):106 doi: 10.12178/1001-0548.2020068
    [53]
    WU Z N,DI Z R,FAN Y. The robustness of interdependent directed networks with intra-layer angular correlations[J]. Frontiers in Physics,2021,9:755567 doi: 10.3389/fphy.2021.755567
    [54]
    ZHANG A B,ZENG A,FAN Y,et al. Detangling the multilayer structure from an aggregated network[J]. New Journal of Physics,2021,23(7):073046 doi: 10.1088/1367-2630/ac136d
    [55]
    LIU J,FAN Y,ZHANG J,et al. Coevolution of agent’s behavior and noise parameters in majority vote game on multilayer networks[EB/OL]. (2019-01-31)[2023-03-31]. https://iopscience.iop.org/article/10.1088/1367-2630/ab00aa
    [56]
    YUAN Z Z,ZHAO C,WANG W X,et al. Exact controllability of multiplex networks[J]. New Journal of Physics,2014,16(10):103036 doi: 10.1088/1367-2630/16/10/103036
    [57]
    LI L B,FAN Y,ZENG A,et al. Understanding the anticontagion process and reopening of China during COVID-19 via coevolution network of epidemic and awareness[J]. Complexity,2021,2021:1
    [58]
    ZENG A,YEUNG C H,MEDO M,et al. Modeling mutual feedback between users and recommender systems[J]. Journal of Statistical Mechanics:Theory and Experiment,2015,2015(7):P07020 doi: 10.1088/1742-5468/2015/07/P07020
    [59]
    ZENG A,YEUNG C H. Predicting the future trend of popularity by network diffusion[J]. Chaos,2016,26(6):063102 doi: 10.1063/1.4953013
    [60]
    ZHOU L,CUI X H,ZENG A,et al. Improving diffusion-based recommendation in online rating systems[J]. International Journal of Modern Physics C,2021,32(7):2150094 doi: 10.1142/S0129183121500947
    [61]
    LI H Y,ZENG A. Improving recommendation by connecting user behavior in temporal and topological dimensions[J]. Physica A:Statistical Mechanics and Its Applications,2022,585:126378 doi: 10.1016/j.physa.2021.126378
    [62]
    吴宗柠,樊瑛. 复杂网络视角下国际贸易研究综述[J]. 电子科技大学学报,2018,47(3):469 doi: 10.3969/j.issn.1001-0548.2018.03.023
    [63]
    FAN Y,REN S T,CAI H B,et al. The state’s role and position in international trade:a complex network perspective[J]. Economic Modelling,2014,39:71 doi: 10.1016/j.econmod.2014.02.027
    [64]
    任素婷,梁栋,樊瑛. 国际贸易网络中国家地位演化的聚类分析[J]. 北京师范大学学报(自然科学版),2014,50(3):323
    [65]
    任素婷,崔雪锋,樊瑛. 国际贸易网络中的靴襻渗流模型[J]. 电子科技大学学报,2015,44(2):178
    [66]
    吴宗柠,吕俊宇,蔡宏波,等. 双曲空间下国际贸易网络建模与分析:以小麦国际贸易为例[J]. 复杂系统与复杂性科学,2018,15(1):31
    [67]
    WU Z N,CAI H B,ZHAO R N,et al. A topological analysis of trade distance:evidence from the gravity model and complex flow networks[J]. Sustainability,2020,12(9):3511 doi: 10.3390/su12093511
    [68]
    程静静,樊瑛. 基于网络相似性测度的国际贸易产品分类[J]. 电子科技大学学报,2021,50(2):303
    [69]
    吴畏,王文旭,樊瑛. 基于风险传染的金融网络系统风险模型[J]. 北京师范大学学报(自然科学版),2014,50(6):668
    [70]
    李恺华,樊瑛. 基于优化阈值法的股票网络构建与重要节点判别[J]. 北京师范大学学报(自然科学版),2015,51(6):582
    [71]
    LI X M,HUANG S Y,CHEN Q H. Analyzing the driving and dragging force in China’s inter-provincial migration flows[J]. International Journal of Modern Physics C,2019,30(7):1940015 doi: 10.1142/S0129183119400151
    [72]
    LI X M,HUANG S Y,CHEN J W,et al. Analysis of the driving factors of U. S. domestic population mobility[J]. Physica A: Statistical Mechanics and Its Applications,2020,539:122984 doi: 10.1016/j.physa.2019.122984
    [73]
    LI X M,XU H Z,CHEN J W,et al. Characterizing the international migration barriers with a probabilistic multilateral migration model[J]. Scientific Reports,2016,6:32522 doi: 10.1038/srep32522
    [74]
    GOU W S,HUANG S Y,CHEN Q H,et al. Structure and dynamic of global population migration network[J]. Complexity,2020,2020:1
    [75]
    HUANG S Y,LI X M,CHEN Q H. Longcuts in the global migration network[J]. EPL (Europhysics Letters),2021,134(4):48002 doi: 10.1209/0295-5075/134/48002
    [76]
    HE Y F,ZHAO C,ZENG A. Ranking locations in a city via the collective home-work relations in human mobility data[EB/OL]. (2022-12-15)[2023-04-01]. https://www.sciencedirect.com/science/article/abs/pii/S037843712200841X
    [77]
    ZENG F Q,GONG L,LIU J,et al. Human mobility in interest space and interactive random walk[J]. Journal of Physics:Complexity,2020,1(2):025004 doi: 10.1088/2632-072X/ab7f4f
    [78]
    WANG M Y,ZENG A,CUI X H. Collective user switching behavior reveals the influence of TV channels and their hidden community structure[J]. Physica A: Statistical Mechanics and Its Applications,2022,606:128105 doi: 10.1016/j.physa.2022.128105
    [79]
    SHI Y B,LI L,WANG Y G,et al. A study of Chinese regional hierarchical structure based on surnames[J]. Physica A:Statistical Mechanics and Its Applications,2019,518:169 doi: 10.1016/j.physa.2018.11.059
    [80]
    SHI Y B,LI L,WANG Y G,et al. Regional surname affinity:a spatial network approach[J]. American Journal of Physical Anthropology,2019,168(3):428 doi: 10.1002/ajpa.23755
    [81]
    ZENG A,SHEN Z S,ZHOU J L,et al. The science of science:from the perspective of complex systems[J]. Physics Reports,2017,714/715:1 doi: 10.1016/j.physrep.2017.10.001
    [82]
    周建林,牛琪锴,曾安,等. 基于复杂网络视角的科学文献数据分析[J]. 科技导报,2018,36(8):55
    [83]
    YAO L Y,WEI T,ZENG A,et al. Ranking scientific publications:the effect of nonlinearity[J]. Scientific Reports,2014,4:6663 doi: 10.1038/srep06663
    [84]
    ZHOU J L,ZENG A,FAN Y,et al. Ranking scientific publications with similarity-preferential mechanism[J]. Scientometrics,2016,106(2):805 doi: 10.1007/s11192-015-1805-1
    [85]
    WANG Y N,ZENG A,FAN Y,et al. Ranking scientific publications considering the aging characteristics of citations[EB/OL]. (2019-05-24)[2023-04-02]. https://link.springer.com/article/10.1007/s11192-019-03117-9
    [86]
    NIU Q K,ZHOU J L,ZENG A,et al. Which publication is your representative work?[J]. Journal of Informetrics,2016,10(3):842 doi: 10.1016/j.joi.2016.06.001
    [87]
    CUI H C,ZENG A,FAN Y,et al. Identifying the key reference of a scientific publication[EB/OL]. (2020-06-21)[2023-04-03]. https://link.springer.com/article/10.1007/s11518-020-5455-3
    [88]
    SHEN Z S,YANG L Y,PEI J S,et al. Interrelations among scientific fields and their relative influences revealed by an input-output analysis[J]. Journal of Informetrics,2016,10(1):82 doi: 10.1016/j.joi.2015.11.002
    [89]
    SONG D Q,WANG W P,FAN Y,et al. Quantifying the structural and temporal characteristics of negative links in citation networks[J]. Information Processing and Management,2022,59(4):102996 doi: 10.1016/j.ipm.2022.102996
    [90]
    ZHOU J L,ZENG A,FAN Y,et al. Identifying important scholars via directed scientific collaboration networks[J]. Scientometrics,2018,114(3):1327 doi: 10.1007/s11192-017-2619-0
    [91]
    ZENG A,FAN Y,DI Z R,et al. Fresh teams are associated with original and multidisciplinary research[J]. Nature Human Behaviour,2021,5(10):1314 doi: 10.1038/s41562-021-01084-x
    [92]
    WANG F H,FAN Y,ZENG A,et al. A nonlinear collective credit allocation in scientific publications[J]. Scientometrics,2019,119(3):1655 doi: 10.1007/s11192-019-03107-x
    [93]
    XING Y M,WANG F H,ZENG A,et al. Solving the cold-start problem in scientific credit allocation[J]. Journal of Informetrics,2021,15(3):101157 doi: 10.1016/j.joi.2021.101157
    [94]
    ZENG A,SHEN Z S,ZHOU J L,et al. Increasing trend of scientists to switch between topics[J]. Nature Communications,2019,10:3439 doi: 10.1038/s41467-019-11401-8
    [95]
    ZENG A,FAN Y,DI Z R,et al. Impactful scientists have higher tendency to involve collaborators in new topics[J]. Proceedings of the National Academy of Sciences of the United States of America,2022,119(33):e2207436119
    [96]
    LI H Y,WU M J,WANG Y G,et al. Bibliographic coupling networks reveal the advantage of diversification in scientific projects[J]. Journal of Informetrics,2022,16(3):101321 doi: 10.1016/j.joi.2022.101321
    [97]
    GENG Z J,ZHANG Y W,LU B,et al. Network-synchronization analysis reveals the weakening tropical circulations[J]. Geophysical Research Letters,2021,48(11):e2021GL093582 doi: 10.1029/2021GL093582
    [98]
    FAN J F,MENG J,LUDESCHER J,et al. Network-based approach and climate change benefits for forecasting the amount of Indian monsoon rainfall[J]. Journal of Climate,2022,35(3):1009 doi: 10.1175/JCLI-D-21-0063.1
    [99]
    YAN X Y,FAN Y,DI Z R,et al. Efficient learning strategy of Chinese characters based on network approach[J]. PLoS One,2013,8(8):e69745 doi: 10.1371/journal.pone.0069745
    [100]
    LIN G Q,AO B,CHEN J W,et al. Modeling and controlling the two-phase dynamics of the p53 network:a Boolean network approach[J]. New Journal of Physics,2014,16:125010 doi: 10.1088/1367-2630/16/12/125010
  • Cited by

    Periodical cited type(7)

    1. 夏爽,张瑶,方田红. 基于变点检测的上海旅游流网络时空特征研究(英文). Journal of Resources and Ecology. 2025(02): 546-557 .
    2. 高玉琴,徐诺,汪键,王丽君,刘彤. 基于复杂网络的洪涝灾害链风险评估. 水资源保护. 2025(02): 115-122 .
    3. 张正勇,苏健生,姜敏勤,杨钰. 基于邻域信息熵与有效距离的网络节点识别. 南京航空航天大学学报(自然科学版). 2025(02): 387-396 .
    4. 江成,聂丽君,张嘉诚. 复杂系统视角下新质生产力特征及内涵分析框架研究. 技术经济与管理研究. 2024(04): 1-7 .
    5. 单雨濛,张科,胡文军,何英,郑泽宇. 基于超图的中药方剂超网络中药材群组信息挖掘. 中草药. 2024(11): 3816-3824 .
    6. 柴正猛,宋璐璐. 基于中国东盟货运航线数据的网络结构与鲁棒性. 科技和产业. 2024(12): 211-217 .
    7. 王志军,苏晨予,余新宇. 面向数智化异构学习环境的学习者交互多层网络分析——基于Traces分析框架的应用与展望. 远程教育杂志. 2024(06): 73-82+91 .

    Other cited types(21)

Catalog

    Article Metrics

    Article views (491) PDF downloads (175) Cited by(28)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return