Please wait a minute...
Chin. Phys. B, 2024, Vol. 33(5): 058901    DOI: 10.1088/1674-1056/ad20d6
INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY Prev   Next  

Identifying influential spreaders in complex networks based on density entropy and community structure

Zhan Su(苏湛), Lei Chen(陈磊)†, Jun Ai(艾均), Yu-Yu Zheng(郑雨语), and Na Bie(别娜)
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Abstract  In recent years, exploring the relationship between community structure and node centrality in complex networks has gained significant attention from researchers, given its fundamental theoretical significance and practical implications. To address the impact of network communities on target nodes and effectively identify highly influential nodes with strong propagation capabilities, this paper proposes a novel influential spreaders identification algorithm based on density entropy and community structure (DECS). The proposed method initially integrates a community detection algorithm to obtain the community partition results of the networks. It then comprehensively considers the internal and external density entropies and degree centrality of the target node to evaluate its influence. Experimental validation is conducted on eight networks of varying sizes through susceptible-infected-recovered (SIR) propagation experiments and network static attack experiments. The experimental results demonstrate that the proposed method outperforms five other node centrality methods under the same comparative conditions, particularly in terms of information spreading capability, thereby enhancing the accurate identification of critical nodes in networks.
Keywords:  complex networks      influential spreaders      propagation model      static attack  
Received:  17 October 2023      Revised:  17 January 2024      Accepted manuscript online:  22 January 2024
PACS:  89.75.Fb (Structures and organization in complex systems)  
Fund: Project supported by the National Natural Science Foundation of China (Grant No. 61803264).
Corresponding Authors:  Lei Chen     E-mail:  213330655@st.usst.edu.cn

Cite this article: 

Zhan Su(苏湛), Lei Chen(陈磊), Jun Ai(艾均), Yu-Yu Zheng(郑雨语), and Na Bie(别娜) Identifying influential spreaders in complex networks based on density entropy and community structure 2024 Chin. Phys. B 33 058901

[1] Liu Y, Song A, Shan X, Xue Y and Jin J 2022 Expert Syst. Appl. 196 116557
[2] Munikoti S, Das L and Natarajan B 2022 Neurocomputing 468 211
[3] Tikka V, Haapaniemi J, Räisänen O and Honkapuro S 2022 Appl. Energy 328 120124
[4] Zhang H, Fiszman M, Shin D, Miller C M, Rosemblat G and Rindflesch T C 2011 J. Biomed. Inform. 44 830
[5] Liu Y, Jiang M, Hu L and He Z 2023 J. Informetr. 17 101424
[6] Freeman L C 1977 Sociometry 40 35
[7] Brandes U, Borgatti S P and Freeman L C 2016 Soc. Netw. 44 153
[8] Xu Q, Sun L Z and Bu C J 2023 Chaos Solitons Fractals 173 113753
[9] Zhang Q, Tang R, Yao Z and Zhang Z B 2023 Appl. Math. Comput. 459 128276
[10] Qiu L, Zhang J and Tian X 2021 Appl. Intell. 51 4394
[11] Asgharian Rezaei A, Munoz J, Jalili M and Khayyam H 2023 Expert Syst. Appl. 214 119086
[12] Zhao G, Jia P, Zhou A and Zhang B 2020 Neurocomputing 414 18
[13] Li Z, Ren T, Ma X, Liu S, Zhang Y and Zhou T 2019 Sci. Rep. 9 8387
[14] Yang X and Xiao F 2021 Knowl-Based Syst. 227 107198
[15] Li Z and Huang X 2022 Sci. Rep. 12 9879
[16] Zhao Z, Li D, Sun Y, Zhang R and Liu J 2023 Knowl-Based Syst. 260 110163
[17] Han Z M, Wu Y, Tan X S, Duan D G and Yang W J 2015 Acta Phys. Sin. 64 058902 (in Chinese)
[18] Jiang Y, Yang S Q, Yan Y W, Tong T C and Dai J Y 2022 Chin. Phys. B 31 058903
[19] Han Z M, Chen Y, Li M Q, Liu W and Yang W J 2016 Acta Phys. Sin. 65 168901 (in Chinese)
[20] Ma X J and Ma Y H 2019 Complexity 2019 9057194
[21] Zhong L F, Bai Y, Tian Y, Luo C, Huang J and Pan W J 2021 Complexity 2021 5554322
[22] Wang T T, Liang Z W and Zhang R X 2023 Acta Phys. Sin. 72 048901 (in Chinese)
[23] Kumar S, Kumar A and Panda B S 2023 IEEE Trans. Ind. Inf. 19 703
[24] Sun P G, Miao Q and Staab S 2021 Pattern Recognition 120 108130
[25] Zhao Z J, Guo Q, Yu K and Liu J G 2020 Physica A 551 123893
[26] Pons P and Latapy M 2006 J. Graph Algorithms Appl. 10 191
[27] Hu F and Liu Y 2016 Physica A 446 138
[28] Saraiva P 2023 Kuwait J. Sci. 50 194
[29] Li X, Zhang X, Zhao C and Duan X 2021 Chaos 31 051104
[30] Zhu L, Yang F, Guan G and Zhang Z 2021 Inf. Sci. 562 240
[31] Gupta M and Mishra R 2021 Decis. Support Syst. 149 113608
[32] Yang X H, Xiong Z, Ma F, Chen X, Ruan Z, Jiang P and Xu X 2021 Physica A 573 125971
[33] Rossi R and Ahmed N 2015 Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, January 25-30, 2015, Austin, Texas, USA, p. 4292
[34] Yang K, Liu Y, Zhao Z, Zhou X, Ding P 2023 Eur. Phys. J. B 96 27
[35] Liu C, Wang P and Chen A 2021 40th Chinese Control Conference (CCC), July 26-28, 2021, Shanghai, China, p. 736
[36] Watts D J and Strogatz S H 1998 Nature 393 440
[37] Barabási A L and Albert R 1999 Science 286 509
[38] Leskovec J, Kleinberg J and Faloutsos C 2007 ACM Trans. Knowl. Discov. Data 1 2
[39] Freeman L C 1978 Soc. Netw. 1 215
[40] Kitsak M, Gallos L K, Havlin S, Liljeros F, Muchnik L, Stanley H E and Makse H A 2010 Nat. Phys. 6 888
[41] Wang B, Zhang J, Dai J and Sheng J 2022 Sci. Rep. 12 1833
[42] Ai J, He T, Su Z and Shang L H 2022 Chaos Solitons Fractals 164 112627
[43] Kumar S, Mallik A and Panda B S 2022 World Wide Web 25 2487
[44] Yuan Z W, Lv C C, Si S B and Duan D L 2021 Chin. Phys. B 30 050501
[45] Zhou F, Yuan Y and Zhang M 2019 Arab. J. Sci. Eng. 44 2837
[1] Prediction of collapse process and tipping points for mutualistic and competitive networks with k-core method
Dongli Duan(段东立), Feifei Bi(毕菲菲), Sifan Li(李思凡), Chengxing Wu(吴成星), Changchun Lv(吕长春), and Zhiqiang Cai(蔡志强). Chin. Phys. B, 2024, 33(5): 050201.
[2] Effects of individual heterogeneity on social contagions
Fu-Zhong Nian(年福忠) and Yu Yang(杨宇). Chin. Phys. B, 2024, 33(5): 058705.
[3] A multilayer network diffusion-based model for reviewer recommendation
Yiwei Huang(黄羿炜), Shuqi Xu(徐舒琪), Shimin Cai(蔡世民), and Linyuan Lü(吕琳媛). Chin. Phys. B, 2024, 33(3): 038901.
[4] Source localization in signed networks with effective distance
Zhi-Wei Ma(马志伟), Lei Sun(孙蕾), Zhi-Guo Ding(丁智国), Yi-Zhen Huang(黄宜真), and Zhao-Long Hu(胡兆龙). Chin. Phys. B, 2024, 33(2): 028902.
[5] Self-similarity of complex networks under centrality-based node removal strategy
Dan Chen(陈单), Defu Cai(蔡德福), and Housheng Su(苏厚胜). Chin. Phys. B, 2023, 32(9): 098903.
[6] Identifying multiple influential spreaders in complex networks based on spectral graph theory
Dong-Xu Cui(崔东旭), Jia-Lin He(何嘉林), Zi-Fei Xiao(肖子飞), and Wei-Ping Ren(任卫平). Chin. Phys. B, 2023, 32(9): 098904.
[7] Important edge identification in complex networks based on local and global features
Jia-Hui Song(宋家辉). Chin. Phys. B, 2023, 32(9): 098901.
[8] Stability and multistability of synchronization in networks of coupled phase oscillators
Yun Zhai(翟云), Xuan Wang(王璇), Jinghua Xiao(肖井华), and Zhigang Zheng(郑志刚). Chin. Phys. B, 2023, 32(6): 060503.
[9] Identification of key recovering node for spatial networks
Zijian Yan(严子健), Yongxiang Xia(夏永祥), Lijun Guo(郭丽君), Lingzhe Zhu(祝令哲), Yuanyuan Liang(梁圆圆), and Haicheng Tu(涂海程). Chin. Phys. B, 2023, 32(6): 068901.
[10] AG-GATCN: A novel method for predicting essential proteins
Peishi Yang(杨培实), Pengli Lu(卢鹏丽), and Teng Zhang(张腾). Chin. Phys. B, 2023, 32(5): 058902.
[11] Analysis of cut vertex in the control of complex networks
Jie Zhou(周洁), Cheng Yuan(袁诚), Zu-Yu Qian(钱祖燏), Bing-Hong Wang(汪秉宏), and Sen Nie(聂森). Chin. Phys. B, 2023, 32(2): 028902.
[12] SLGC: Identifying influential nodes in complex networks from the perspectives of self-centrality, local centrality, and global centrality
Da Ai(艾达), Xin-Long Liu(刘鑫龙), Wen-Zhe Kang(康文哲), Lin-Na Li(李琳娜), Shao-Qing Lü(吕少卿), and Ying Liu(刘颖). Chin. Phys. B, 2023, 32(11): 118902.
[13] Vertex centrality of complex networks based on joint nonnegative matrix factorization and graph embedding
Pengli Lu(卢鹏丽) and Wei Chen(陈玮). Chin. Phys. B, 2023, 32(1): 018903.
[14] Ergodic stationary distribution of a stochastic rumor propagation model with general incidence function
Yuhuai Zhang(张宇槐) and Jianjun Zhu(朱建军). Chin. Phys. B, 2022, 31(6): 060202.
[15] Correlation and trust mechanism-based rumor propagation model in complex social networks
Xian-Li Sun(孙先莉), You-Guo Wang(王友国), and Lin-Qing Cang(仓林青). Chin. Phys. B, 2022, 31(5): 050202.
No Suggested Reading articles found!