[1] GOWTHAM A, ANUJ K, VIPIN K. Spatio-temporal data mining: a survey of problems and methods[J]. ACM Computing Surveys, 2018, 51(4): 83.
[2] JENG T H, CHEN Y M, CHEN C C, et al. MD-MinerP: interaction profiling bipartite graph mining for malware-control domain detection[J]. Security and Communication Networks, 2020, 2020: 8841544.
[3] ZHANG L L, ZHAO M H, ZHAO D Z. Bipartite graph link prediction method with homogeneous nodes similarity for music recommendation[J]. Multimedia Tools and Applications, 2020, 79(19/20): 13197-13215.
[4] 谭芳, 杨阳, 卓伊玲, 等. 网络隐私争议事件中用户隐私关注及情感对比研究[J]. 图书情报工作, 2021, 65(2): 87-97.
TAN F, YANG Y, ZHUO Y L, et al. Comparison of privacy concern and sentimental characteristics of users in internet privacy controversial events[J]. Library and Information Service, 2021, 65(2): 87-97.
[5] KABAKULAK B, TASKIN Z, PUSANE A. A branch-and-cut algorithm for a bipartite graph construction problem in digital communication systems[J]. Networks, 2020, 75(2): 137-157.
[6] MANOOCHEHRI H, NOURANI M. Drug-target interaction prediction using semi-bipartite graph model and deep learning[J]. BMC Bioinformatics, 2020, 21(4): 248.
[7] WANG Z C, ZHUO M, ARNOLD C. Toward heterogeneous information fusion: bipartite graph convolutional networks for in silico drug repurposing[J]. Bioinformatics, 2020, 36(1): 525-533.
[8] YU Q, CHEN J, DU Y, et al. A method for building a genome-connectome bipartite graph model[J]. Journal of Neuroscience Methods, 2019, 320: 64-71.
[9] 郗强, 唐锡晋.基于社会网络分析的“系统科学大会”可视化探析[J]. 系统科学与数学, 2018, 38(5): 521-536.
XI Q, TANG X J. Visual exploration of CSSC2017 based on social network analysis[J]. Journal of Systems Science and Mathematical Sciences, 2018, 38(5): 521-536.
[10] YANG S Y, NING L J, CAI X L, et al. Dynamic spatiotemporal causality analysis for network traffic flow based on transfer entropy and sliding window approach[J]. Joural of Advanced Transportation, 2021, 2021: 6616800.
[11] JIANG C M, MAO Y F, CHAI Y, et al. Day-ahead renewable scenario forecasts based on generative adversarial networks[J]. International Journal of Energy Research, 2020, 45(5): 7572-7587.
[12] WANG Y, TAYLOR J E. Coupling sentiment and human mobility in natural disasters: a twitter-based study of the 2014 south napa earthquake[J]. Natural Hazards, 2018, 92(2): 907-925.
[13] CHARAKOPOULOS A K, KATSOULI G A, KARAKASIDIS T E. Dynamics and causalities of atmospheric and oceanic data identified by complex networks and Granger causality analysis[J]. Physica A-Statistical Mechanics and Its Applications, 2018, 495(C): 436-453.
[14] SMITH S, FOX P, MILLER K, et al. Correspondence of the brain's functional architecture during activation and rest[J]. Proceedings of the National Academy of Sciences of the United States of America, 2009, 106(31): 13040-13045.
[15] BELLEC P, PERLBARG V, JBABDI S, et al. Identification of large-scale networks in the brain using fMRI[J]. NeuroImage, 2006, 29(4):1231-1243.
[16] WANG L N, CHENG Y Y, ZANG C R. A symbolized time series network based on seasonal-trend-loess method[J]. Acta Physica Sinica, 2019, 68(23): 238901.
[17] WANG X, ZHOU Z M, XIAO F, et al. Spatio-temporal analysis and predictionof cellular traffic in metropolis[J]. IEEE Transactions on Mobile Computing, 2019, 18(9): 2190-2202.
[18] LI L Q, YANG Z A, NAI H, et al. Locally weighted adjustable parameter-based LPVG in the identification of functional regions[J]. 2019, 7: 99988-100002.
[19] ENAMI S, SHIOMOTO K. Spatio-temporal human mobility prediction based on trajectory data mining for resource management in mobile communication networks[C]// IEEE. 2019 IEEE 20th International Conference on High Performance Switching and Routing (HPSR). Xi'an, China, 2019:8808106.
[20] BORGETTI S P, EVERETT M G. Models of core/periphery structures[J]. Social Networks, 1999, 21(4): 375-395.
[21] BORGETTI S P, EVERETT M G. Network analysis of 2-mode data[J]. Social Networks, 1997, 19(3): 243-269.
[22] WANG L N, WANG K, SHEN J L. Weighted complex networks in urban public transportation: modeling and testing[J]. Physica A-Statistical Mechanics and Its Applications, 2020, 545: 123498.
[23] NEWMAN M E J. Networks: an Introduction[M]. New York: Oxford University Press, 2011: 117-122.
[24] 郑伯铭, 刘安乐, 韩剑磊, 等. 云南省旅游经济联系网络结构演化与协同发展模式建构[J]. 经济地理, 2021, 41(2): 222-231.
ZHENG B M, LIU A L, HAN J L, et al. The structural evolution of Yunnan tourism economic contact network and the construction of cooperative development model[J]. Economic Geography, 2021, 41(2): 222-231.
[25] 初楠臣, 张平宇, 吴相利,等. 基于日流量视角的俄罗斯首府城市铁路客运网络空间特征[J]. 地理研究, 2021, 40(1): 247-262.
CHU N C, ZHANG P Y, WU X L, et al. The spatial characteristics of railway passenger network in Russian capital cities based on the daily railway passenger flow[J]. Geographical Research, 2021, 40(1): 247-262.
[26] YU J, MA J. Social network analysis as a tool for the analysis of the international trade network of aquatic products[J]. Aquaculture International, 2020, 28(3): 1195-1211.
[27] GARCIA A, DUVA M, MOLLAOGLU S, et al. Expertise flows and network structures in AEC project teams[C]// GRAU D, TANG P, ELASMAR M. Construction Research Congress. Arizona State Univ, Del E Webb Sch Construct, Tempe, AZ, 2020: 95-104.
