《山东大学学报(理学版)》 ›› 2022, Vol. 57 ›› Issue (8): 1-12.doi: 10.6040/j.issn.1671-9352.0.2022.066
• • 下一篇
范敏1,2,罗杉1,2,李金海1,2*
FAN Min1,2, LUO Shan1,2, LI Jin-hai1,2*
摘要: 网络上的概念认知是网络数据分析领域的一个重要方向。从形式概念分析中的必然算子、可能算子出发,提出变精度可能算子,表明经典可能算子是变精度可能算子的特殊情形;进一步,对可能算子的性质进行研究,并解释它们在传染病网络研究中的意义;接着,根据变精度可能算子提出两种网络广义概念及其上下近似与边界,通过实例说明基于变精度可能算子的上下近似和边界在网络概念认知中具有更丰富的语义;然后,结合复杂网络分析中的网络特征值方法,定义网络弱概念,并提出基于变精度可能算子的网络弱概念获取方法;最后,利用文中算法在UCI数据集上进行测试,结果证实了变精度可能算子在网络概念认知中的优势。
中图分类号:
[1] WILLE R. Restructuring lattice theory: an approach based on hierarchies of concepts[C] //Ordered Sets. Berlin: Springer,1982: 445-470. [2] GANTER B, WILLE R. Formal concept analysis: mathematical foundations[M]. New York: Springer-Verlag, 1999: 18-45. [3] KUMAR C A, ISHWARYA M S, LOO C K. Formal concept analysis approach to cognitive functionalities of bidirectional associative memory[J]. Biologically Inspired Cognitive Architectures, 2015, 12:20-33. [4] HAO Fei, YANG Yixuan, MIN Geyong, et al. Incremental construction of three-way concept lattice for knowledge discovery in social networks[J]. Information Sciences, 2021, 578:257-280. [5] 徐伟华,李金海,魏玲,等. 形式概念分析理论与应用[M].北京: 科学出版社, 2016: 188-205. XU Weihua, LI Jinhai, WEI Ling, et al. Formal concept analysis: theory and application[M]. Beijing: Science Press, 2016: 188-205. [6] 李进金,李克典,吴端恭. 基于粗糙集与概念格的知识系统模型[M]. 北京: 科学出版社, 2013: 119-142. LI Jinjin, LI Kedian, WU Duangong. Knowledge system model based on rough set and concept lattice[M]. Beijing: Science Press, 2013: 119-142. [7] 张文修,吴伟志,梁吉业,等. 粗糙集理论与方法[M]. 北京: 科学出版社, 2003: 12-16. ZHANG Wenxiu, WU Weizhi, LIANG Jiye, et al. Rough set theory and methods[M]. Beijing: Science Press, 2003: 12-16. [8] TONELLA P. Using a concept lattice of decomposition slices for program understanding and impact analysis[J]. IEEE Transactions on Software Engineering, 2003, 29(6):495-509. [9] 朵琳,杨丙. 一种基于用户兴趣概念格的推荐评分预测方法[J]. 小型微型计算机系统, 2020, 41(10):2104-2108. DUO Lin, YANG Bing. Recommendation rating prediction based on user interest concept lattice[J]. Journal of Chinese Computer Systems, 2020, 41(10):2104-2108. [10] ROCCO C M, HERNANDEZ-PERDOMO E, MUN J. Introduction to formal concept analysis and its applications in reliability engineering[J]. Reliability Engineering & System Safety, 2020, 202:107002. [11] ANANIAS K, MISSAOUI R, RUAS P, et al. Triadic concept approximation[J]. Information Sciences, 2021, 572:126-146. [12] ZOU Li, LIN Hongmei, SONG Xiaoying, et al. Rule extraction based on linguistic-valued intuitionistic fuzzy layered concept lattice[J]. International Journal of Approximate Reasoning, 2021, 133:1-16. [13] ZOU Caifeng, DENG Huifang. Using fuzzy concept lattice for intelligent disease diagnosis[J]. IEEE Access, 2017, 5:236-242. [14] CHEN Jianhong, ZHONG Xudong, XU Zitong, et al. Analysis of mine safety performance evaluation law based on matter-element analysis and rough set of concept lattice reduction[J]. IEEE Access, 2021, 9:94169-94180. [15] DUNTSCH I, GEDIGA G. Modal-style operators in qualitative data analysis[C] //Proceedings of the 2002 IEEE International Conference on Data Mining. Washington, D.C.: IEEE, 2002: 155-162. [16] YAO Yiyu. A comparative study of formal concept analysis and rough set theory in data analysis[C] //Proceedings of 4th International Conference on Rough Sets and Current Trends in Computing. Berlin: Springer, 2004: 59-68. [17] 闫梦宇,李金海. 概念格共有与独有属性(对象)的关系研究[J]. 计算机科学与探索, 2019, 13(4):702-710. YAN Mengyu, LI Jinhai. Research on relationship between common and unique attributes(objects)of concept lattice[J]. Journal of Frontiers of Computer Science and Technology, 2019, 13(4):702-710. [18] ZHAO Yingxiu, LI Jinhai, LIU Wenqi, et al. Cognitive concept learning from incomplete information[J]. International Journal of Machine Learning and Cybernetics, 2017, 7(4):159-170. [19] LI Jinhai, HUANG Chenchen, QI Jianjun, et al. Three-way cognitive concept learning via multi-granularity[J]. Information Sciences, 2017, 378:244-263. [20] QIAN Ting, WEI Ling, QI Jianjun. A theoretical study on the object(property)oriented concept lattices based on three-way decisions[J]. Soft Computing, 2019, 23(10):9477-9489. [21] 米允龙,李金海,刘文奇,等. MapReduce框架下的粒概念认知学习系统研究[J]. 电子学报, 2018, 46(2):289-297. MI Yunlong, LI Jinhai, LIU Wenqi. Research on granular concept cognitive learning system under MapReduce framework[J]. Acta Electronica Sinica, 2018, 46(2):289-297. [22] SHI Yong, MI Yunlong, LI Jinhai, et al. Concept-cognitive learning model for incremental concept learning[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(2):809-821. [23] LI Jinhai, LIU Zhiming. Granule description in knowledge granularity and representation[J]. Knowledge-Based Systems, 2020, 203(11):106160. [24] 林聚任. 社会网络分析: 理论、方法与应用[M]. 北京: 北京师范大学出版社, 2009: 306-307. LIN Juren. Social network analysis: theory, methods and applications[M]. Beijing: Beijing Normal University Publishing House, 2009: 306-307. [25] PASTOR-SATORRAS R, CASTELLANO C, MIEGHEM P V, et al. Epidemic processes in complex networks[J]. Reviews of Modern Physics, 2015, 87(3):925-979. [26] WATTS D J, STROGATZ S H. Collective dynamics of ‘small-world’ networks[J]. Nature, 1998, 393(6684):440-442. [27] BARABÁSI A L, ALBERT R. Emergence of scaling in random networks[J]. Science, 1999, 286(5439):509-512. [28] SHARMA N, VERMAC A K, GUPTAA A K. Spatial network based model forecasting transmission and control of COVID-19[J]. Physica A: Statistical Mechanics and its Applications, 2020, 581:126223. [29] MARON B A, ALTUCCI L, BALLIGAND J L, et al. A global network for network medicine[J]. NPJ Systems Biology and Applications, 2020, 6:29. [30] PAUL A, BHATTACHARJEE J K, PAL A, et al. Emergence of universality in the transmission dynamics of COVID-19[J]. Scientific Reports, 2021, 11:18891. [31] GYSI D M, VALLE T D, ZITNIK M, et al. Network medicine framework for identifying drug-repurposing opportunities for COVID-19[J]. Proceedings of the National Academy of Sciences, 2021, 118(19):e2025581118. [32] KERMACK W O, MCKENDRICK A G. A contribution to the mathematical theory of epidemics[J]. Proceedings of the Royal Society of London Series A, 1927, 115(772):700-721. [33] ROMAN H E, CROCCOLO F. Spreading of infections on network models: percolation clusters and random trees[J]. Mathematics, 2021, 9(23):1-22. [34] PASTOR-SATORRAS R, VESPIGNANI A. Immunization of complex networks[J]. Physical Review E, 2001, 65(3):036104. [35] BOGUN A M, PASTOR-SATORRAS R. Epidemic spreading in correlated complex networks[J]. Physical Review E, 2002, 66(4):047104. [36] LEVENTHAL G E, HILL A L, NOWAK M A, et al. Evolution and emergence of infectious diseases in theoretical and real-world networks[J]. Nature Communications, 2015, 6:6101. [37] SCARPINO S V, PETRI G. On the predictability of infectious disease outbreaks[J]. Nature Communications, 2019, 10:898. [38] ZHANG Yuexia, PAN Dawei. Layered SIRS model of information spread in complex networks[J]. Applied Mathematics and Computation, 2021, 411:126524. [39] 马娜,范敏,李金海. 复杂网络下的概念认知学习[J]. 南京大学学报(自然科学), 2019, 55(4):609-623. MA Na, FAN Min, LI Jinhai. Concept-cognitive learning under complex network[J]. Journal of Nanjing University(Natural Science), 2019, 55(4):609-623. [40] 刘文星,范敏,李金海. 网络形式背景下的社区划分方法研究[J]. 计算机科学与探索, 2021, 15(8):1441-1449. LIU Wenxing, FAN Min, LI Jinhai. Research on community division method under network formal context[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(8):1441-1449. |
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