Tian-Fang Zhao, Wei-Neng Chen, Xin-Xin Ma, Xiao-Kun Wu. Evolutionary Computation in Social Propagation over Complex Networks: A Survey. International Journal of Automation and Computing, vol. 18, no. 4, pp.503-520, 2021. https://doi.org/10.1007/s11633-021-1302-3
Citation: Tian-Fang Zhao, Wei-Neng Chen, Xin-Xin Ma, Xiao-Kun Wu. Evolutionary Computation in Social Propagation over Complex Networks: A Survey. International Journal of Automation and Computing, vol. 18, no. 4, pp.503-520, 2021. https://doi.org/10.1007/s11633-021-1302-3

Evolutionary Computation in Social Propagation over Complex Networks: A Survey

doi: 10.1007/s11633-021-1302-3
More Information
  • Author Bio:

    Tian-Fang Zhao received the M. Eng. degree in computer science from Dalian University of Technology, China in 2017. She is currently a Ph. D. degree candidate from School of Computer Science and Engineering, South China University of Technology, China. Her research interests include evolutionary computation, network propagation dynamics, agent-based modeling and simulation, social data analytics. E-mail: tianfang09@foxmail.com ORCID iD: 0000-0002-3520-2951

    Wei-Neng Chen received the B. Eng. and Ph. D. degrees in computer science from Sun Yat-sen University, China in 2006 and 2012, respectively. Since 2016, he has been a full professor with School of Computer Science and Engineering, South China University of Technology, China. He has co-authored over 100 international journal and conference papers, including more than 40 papers published in the IEEE Transactions journals. He was a recipient of the IEEE Computational Intelligence Society (CIS) Outstanding Dissertation Award in 2016, and the National Science Fund for Excellent Young Scholars in 2016. He is currently the Vice-Chair of the IEEE Guangzhou Section. He is also a committee member of the IEEE CIS Emerging Topics Task Force. He serves as an associate editor for IEEE Transactions on Neural Networks and Learning Systems, and Complex & Intelligent Systems. His research interests include computational intelligence, swarm intelligence, network science, and their applications. E-mail: cwnraul634@aliyun.com (Corresponding author) ORCID iD: 0000-0003-0843-5802

    Xin-Xin Ma received the B. Sc. degree in material science and engineering from South China University and Technology, China in 2020. Currently, he is a master student in School of Computer science and Engineering, South China University and Technology, China. His research interests include evolution computation, data-driven optimization, and estimation of distribution algorithms. E-mail: maxinxinscut@163.com

    Xiao-Kun Wu received the Ph. D. degree in communication from Shanghai University, China in 2010. She is currently a professor with School of Journalism & Communication, and a Ph. D. degree candidate from School of Computer Science and Engineering, both in South China University of Technology, China. She has authored three academic books and over 30 research papers in her research areas. Her research interests include online collective actions and communication models, and mass communications. E-mail: wuxiaokun@scut.edu.cn

  • Received Date: 2021-01-07
  • Accepted Date: 2021-04-22
  • Publish Online: 2021-01-04
  • Publish Date: 2021-06-30
  • Social propagation denotes the spread phenomena directly correlated to the human world and society, which includes but is not limited to the diffusion of human epidemics, human-made malicious viruses, fake news, social innovation, viral marketing, etc. Simulation and optimization are two major themes in social propagation, where network-based simulation helps to analyze and understand the social contagion, and problem-oriented optimization is devoted to contain or improve the infection results. Though there have been many models and optimization techniques, the matter of concern is that the increasing complexity and scales of propagation processes continuously refresh the former conclusions. Recently, evolutionary computation (EC) shows its potential in alleviating the concerns by introducing an evolving and developing perspective. With this insight, this paper intends to develop a comprehensive view of how EC takes effect in social propagation. Taxonomy is provided for classifying the propagation problems, and the applications of EC in solving these problems are reviewed. Furthermore, some open issues of social propagation and the potential applications of EC are discussed. This paper contributes to recognizing the problems in application-oriented EC design and paves the way for the development of evolving propagation dynamics.

     

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  • [1]
    M. Saeedmanesh, N. Geroliminis. Dynamic clustering and propagation of congestion in heterogeneously congested urban traffic networks. Transportation Research Procedia, vol. 23, pp. 962–979, 2017. DOI: 10.1016/j.trpro.2017.05.053.
    [2]
    A. Song, W. N. Chen, Y. J. Gong, X. N. Luo, J. Zhang. A divide-and-conquer evolutionary algorithm for large-scale virtual network embedding. IEEE Transactions on Evolutionary Computation, vol. 24, no. 3, pp. 566–580, 2020. DOI: 10.1109/TEVC.2019.2941824.
    [3]
    T. F. Zhao, W. N. Chen, A. W. C. Liew, T. L. Gu, X. K. Wu, J. Zhang. A binary particle swarm optimizer with priority planning and hierarchical learning for networked epidemic control. IEEE Transactions on Systems, Man, and Cybernetics: Systems, published online. DOI: 10.1109/TSMC.2019.2945055.
    [4]
    P. Domingos, M. Richardson. Mining the network value of customers. In Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, San Francisco, USA, pp. 57−66, 2001. DOI: 10.1145/502512.502525.
    [5]
    H. Haehne, K. Schmietendorf, S. Tamrakar, J. Peinke, S. Kettemann. Propagation of wind-power-induced fluctuations in power grids. Physical Review E, vol. 99, no. 5, Article number 050301, 2019. DOI: 10.1103/PhysRevE.99.050301.
    [6]
    K. Li, T. Xu, S. Feng, L. S. Qiao, H. W. Shen, T. Y. Lv, X. Q. Cheng, E. H. Chen. The propagation background in social networks: Simulating and modeling. International Journal of Automation and Computing, vol. 17, no. 3, pp. 353–363, 2020. DOI: 10.1007/s11633-020-1227-2.
    [7]
    B. D. O. Anderson, M. B. Ye. Recent advances in the modelling and analysis of opinion dynamics on influence networks. International Journal of Automation and Computing, vol. 16, no. 2, pp. 129–149, 2019. DOI: 10.1007/s11633-019-1169-8.
    [8]
    R. Antia, R. R. Regoes, J. C. Koella, C. T. Bergstrom. The role of evolution in the emergence of infectious diseases. Nature, vol. 426, no. 6967, pp. 658–661, 2003. DOI: 10.1038/nature02104.
    [9]
    D. A. Caugant, M. C. J. Maiden. Meningococcal carriage and disease − Population biology and evolution. Vaccine, vol. 27, no. S2, pp. B64–B70, 2009. DOI: 10.1016/j.vaccine.2009.04.061.
    [10]
    S. Sen, E. Aydogan, A. I. Aysan. Coevolution of mobile malware and anti-malware. IEEE Transactions on Information Forensics and Security, vol. 13, no. 10, pp. 2563–2574, 2018. DOI: 10.1109/TIFS.2018.2824250.
    [11]
    G. Z. Meng, Y. X. Xue, C. Mahinthan, A. Narayanan, Y. Liu, J. Zhang, T. M. Chen. Mystique: Evolving android malware for auditing anti-malware tools. In Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security, ACM, Xi′an, China, pp. 365−376, 2016. DOI: 10.1145/2897845.2897856.
    [12]
    E. Lieberman, C. Hauert, M. A. Nowak. Evolutionary dynamics on graphs. Nature, vol. 433, no. 7023, pp. 312–316, 2005. DOI: 10.1038/nature03204.
    [13]
    C. Nowzari, V. M. Preciado, G. J. Pappas. Optimal resource allocation for control of networked epidemic models. IEEE Transactions on Control of Network Systems, vol. 4, no. 2, pp. 159–169, 2017. DOI: 10.1109/TCNS.2015.2482221.
    [14]
    S. Han, V. M. Preciado, C. Nowzari, G. J. Pappas. Data-driven network resource allocation for controlling spreading processes. IEEE Transactions on Network Science and Engineering, vol. 2, no. 4, pp. 127–138, 2015. DOI: 10.1109/TNSE.2015.2500158.
    [15]
    T. Yang, X. L Yi, J. F. Wu, Y. Yuan, D. Wu, Z. Y. Meng, Y. G. Hong, H. Wang, Z. L. Lin, K. H. Johansson. A survey of distributed optimization. Annual Reviews in Control, vol. 47, pp. 278–305, 2019. DOI: 10.1016/j.arcontrol.2019.05.006.
    [16]
    K. Drakopoulos, A. Ozdaglar, J. N. Tsitsiklis. An efficient curing policy for epidemics on graphs. IEEE Transactions on Network Science and Engineering, vol. 1, no. 2, pp. 67–75, 2014. DOI: 10.1109/TNSE.2015.2393291.
    [17]
    H. Y. Zheng, J. Wu. Effective network quarantine with minimal restrictions on communication activities. IEEE Transactions on Network Science and Engineering, vol. 3, no. 3, pp. 159–170, 2016. DOI: 10.1109/TNSE.2016.2586751.
    [18]
    I. Tomovski, L. Kocarev. Simple algorithm for virus spreading control on complex networks. IEEE Transactions on Circuits and Systems I:Regular Papers, vol. 59, no. 4, pp. 763–771, 2012. DOI: 10.1109/TCSI.2011.2169853.
    [19]
    X. J. Li, C. Li, X. Li. Minimizing social cost of vaccinating network SIS epidemics. IEEE Transactions on Network Science and Engineering, vol. 5, no. 4, pp. 326–335, 2018. DOI: 10.1109/TNSE.2017.2766665.
    [20]
    J. Del Ser, E. Osaba, D. Molina, X. S. Yang, S. Salcedo-Sanz, D. Camacho, S. Das, P. N. Suganthan, C. A. C. Coello, F. Herrera. Bio-inspired computation: Where we stand and what′s next. Swarm and Evolutionary Computation, vol. 48, pp. 220–250, 2019. DOI: 10.1016/j.swevo.2019.04.008.
    [21]
    S. X. Yang. Evolutionary computation for dynamic optimization problems. In Proceedings of Companion Publication of Annual Conference on Genetic and Evolutionary Computation, ACM, Madrid, Spain, pp. 629−649, 2015. DOI: 10.1145/2739482.2756589.
    [22]
    A. M. Gujarathi, B. V Babu. Evolutionary Computation: Techniques and Applications, Waretown, USA: CRC Press, 2017.
    [23]
    W. N. Chen, J. Zhang, H. S. H. Chung, W. L. Zhong, W. G. Wu, Y. H. Shi. A novel set-based particle swarm optimization method for discrete optimization problems. IEEE Transactions on Evolutionary Computation, vol. 14, no. 2, pp. 278–300, 2010. DOI: 10.1109/TEVC.2009.2030331.
    [24]
    A. Song, W. N. Chen, X. N. Luo, Z. H. Zhan, J. Zhang. Scheduling workflows with composite tasks: A nested particle swarm optimization approach. IEEE Transactions on Services Computing, to be published. DOI: 10.1109/TSC.2020.2975774.
    [25]
    J. Liu, H. A. Abbass, K. C. Tan. Evolutionary Computation and Complex Networks, Cham, Germany: Springer, 2019. DOI: 10.1007/978-3-319-60000-0.
    [26]
    J. H. Holland. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, Cambridge, USA: MIT Press, 1992.
    [27]
    H. P. Schwefel. Numerische Optimierung von Computer-Modellen Mittels der Evolutionsstrategie: Mit Einer Vergleichenden Einführung in Die Hill-Climbing-und Zufallsstrategie, Stuttgart: Birkhäuser, 1977. DOI: 10.1007/978-3-0348-5927-1.
    [28]
    L. J. Fogel, A. J. Owens, M. J. Walsh. Artificial Intelligence Through Simulated Evolution, New York, USA: John Wiley & Sons, 1966.
    [29]
    R. Storn. On the usage of differential evolution for function optimization. In Proceedings of North American Fuzzy Information Processing, IEEE, Berkeley, USA, pp. 519−523, 1996. DOI: 10.1109/NAFIPS.1996.534789.
    [30]
    J. O. Kephart. A biologically inspired immune system for computers. In Proceedings of the 4th International Workshop on the Synthesis and Simulation of Living Systems, Cambridge, USA, 1994.
    [31]
    M. Lahiri, M. Cebrián. The genetic algorithm as a general diffusion model for social networks. In Proceedings of the 24th AAAI Conference on Artificial Intelligence, Atlanta, Georgia, USA, 2010.
    [32]
    A. K. Qin, P. N. Suganthan. Self-adaptive differential evolution algorithm for numerical optimization. In Proceedings of IEEE Congress on Evolutionary Computation, IEEE, Edinburgh, UK, 1785−1791, 2005. DOI: 10.1109/CEC.2005.1554904.
    [33]
    M. Dorigo. Optimization, Learning and Natural Algorithms, Ph. D. dissertation, Polytechnic University of Milan, Italy, 1992.
    [34]
    Q. Yang, W. N. Chen, Z. T. Yu, T. L. Gu, Y. Li, H. X. Zhang, J. Zhang. Adaptive multimodal continuous ant colony optimization. IEEE Transactions on Evolutionary Computation, vol. 21, no. 2, pp. 191–205, 2017. DOI: 10.1109/TEVC.2016.2591064.
    [35]
    M. Dorigo, L. M. Gambardella. Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 53–66, 1997. DOI: 10.1109/4235.585892.
    [36]
    T. Stützle, H. H. Hoos. MAX-MIN ant system. Future Generation Computer Systems, vol. 16, no. 8, pp. 889–914, 2000. DOI: 10.1016/S0167-739X(00)00043-1.
    [37]
    J. Kennedy, R. Eberhart. Particle swarm optimization. In Proceedings of International Conference on Neural Networks, IEEE, Perth, Australia, pp. 1942−1948, 1995. DOI: 10.1109/ICNN.1995.488968.
    [38]
    Q. Yang, W. N. Chen, J. Da Deng, Y. Li, T. L. Gu, J. Zhang. A level-based learning swarm optimizer for large-scale optimization. IEEE Transactions on Evolutionary Computation, vol. 22, no. 4, pp. 578–594, 2018. DOI: 10.1109/TEVC.2017.2743016.
    [39]
    D. Karaboga. An Idea Based on Honey Bee Swarm for Numerical Optimization, Technical Report-TR06, Erciyes University, Turkey, 2005.
    [40]
    Q. F. Zhang, H. Li. MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, vol. 11, no. 6, pp. 712–731, 2007. DOI: 10.1109/TEVC.2007.892759.
    [41]
    K. Deb, S. Agrawal, A. Pratap, T. Meyarivan. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In Proceedings of the 6th International Conference on Parallel Problem Solving from Nature, Springer, Paris, France, pp. 849−858, 2000. DOI: 10.1007/3-540-45356-3_83.
    [42]
    X. Y. Wen, W. N. Chen, Y. Lin, T. L. Gu, H. X. Zhang, Y. Li, Y. L. Yin, J. Zhang. A maximal clique based multiobjective evolutionary algorithm for overlapping community detection. IEEE Transactions on Evolutionary Computation, vol. 21, no. 3, pp. 363–377, 2017. DOI: 10.1109/TEVC.2016.2605501.
    [43]
    H. Zhao, Z. H. Zhan, Y. Lin, X. F. Chen, X. N. Luo, J. Zhang, S. Kwong, J. Zhang. Local binary pattern-based adaptive differential evolution for multimodal optimization problems. IEEE Transactions on Cybernetics, vol. 50, no. 7, pp. 3343–3357, 2020. DOI: 10.1109/TCYB.2019.2927780.
    [44]
    F. F. Wei, W. N. Chen, Q. Yang, J. Deng, X. N. Luo, H. Jin, J. Zhang. A classifier-assisted level-based learning swarm optimizer for expensive optimization. IEEE Transactions on Evolutionary Computation, vol. 25, no. 2, pp. 219–233, 2021. DOI: 10.1109/TEVC.2020.3017865.
    [45]
    W. N. Chen, Y. H. Jia, F. Zhao, X. N. Luo, X. D. Jia, J. Zhang. A cooperative co-evolutionary approach to large-scale multisource water distribution network optimization. IEEE Transactions on Evolutionary Computation, vol. 23, no. 5, pp. 842–857, 2019. DOI: 10.1109/TEVC.2019.2893447.
    [46]
    D. Duvivier, P. Preux, E. G. Talbi. Climbing up NP-hard hills. In Proceedings of International Conference on Parallel Problem Solving from Nature, Springer, Berlin, Germany, pp. 574−583, 1996. DOI: 10.1007/3-540-61723-X_1021.
    [47]
    T. J. Liao, K. Socha, M. A. M. de Oca, T. Stützle, M. Dorigo. Ant colony optimization for mixed-variable optimization problems. IEEE Transactions on Evolutionary Computation, vol. 18, no. 4, pp. 503–518, 2014. DOI: 10.1109/TEVC.2013.2281531.
    [48]
    Z. Christoff, J. U. Hansen. A logic for diffusion in social networks. Journal of Applied Logic, vol. 13, no. 1, pp. 48–77, 2015. DOI: 10.1016/j.jal.2014.11.011.
    [49]
    X. Y. Wang, T. F. Zhao, X. M. Qin. Model of epidemic control based on quarantine and message delivery. Physica A:Statistical Mechanics and its Applications, vol. 458, pp. 168–178, 2016. DOI: 10.1016/j.physa.2016.04.009.
    [50]
    Y. J. Gong, W. N. Chen, Z. H. Zhan, J. Zhang, Y. Li, Q. F. Zhang, J. J, L i. Distributed evolutionary algorithms and their models: A survey of the state-of-the-art. Applied Soft Computing, vol. 34, pp. 286–300, 2015. DOI: 10.1016/j.asoc.2015.04.061.
    [51]
    S. V Buldyrev, R. Parshani, G. Paul, H. E. Stanley, S. Havlin. Catastrophic cascade of failures in interdependent networks. Nature, vol. 464, no. 7291, pp. 1025–1028, 2010. DOI: 10.1038/nature08932.
    [52]
    D. J. Xu, M. L. Lee, W. Hsu. Propagation mechanism for deep and wide neural networks. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Long Beach, USA, pp. 9212−9220. DOI: 10.1109/CVPR.2019.00944.
    [53]
    H. W. Hethcote. The mathematics of infectious diseases. SIAM Review, vol. 42, no. 4, pp. 599–653, 2000. DOI: 10.1137/S0036144500371907.
    [54]
    C. Nowzari, V. M. Preciado, G. J. Pappas. Analysis and control of epidemics: A survey of spreading processes on complex networks. IEEE Control Systems Magazine, vol. 36, no. 1, pp. 26–46, 2016. DOI: 10.1109/MCS.2015.2495000.
    [55]
    T. F. Zhao, W. N. Chen, S. Kwong, T. L. Gu, H. Q. Yuan, J. Zhang, J. Zhang. Evolutionary divide-and-conquer algorithm for virus spreading control over networks. IEEE Transactions on Cybernetics, published online. DOI: 10.1109/TCYB.2020.2975530.
    [56]
    C. Fraser, K. Lythgoe, G. E. Leventhal, G. Shirreff, T. D. Hollingsworth, S. Alizon, S. Bonhoeffer. Virulence and pathogenesis of HIV-1 infection: An evolutionary perspective. Science, vol. 343, no. 6177, Article number 1243727, 2014. DOI: 10.1126/science.1243727.
    [57]
    M. I. Nelson, E. C. Holmes. The evolution of epidemic influenza. Nature Reviews Genetics, vol. 8, no. 3, pp. 196–205, 2007. DOI: 10.1038/nrg2053.
    [58]
    G. E. Leventhal, A. L. Hill, M. A. Nowak, S. Bonhoeffer. Evolution and emergence of infectious diseases in theoretical and real-world networks. Nature Communications, vol. 6, Article number 6101, 2015. DOI: 10.1038/ncomms7101.
    [59]
    K. M. Passino. Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems Magazine, vol. 22, no. 3, pp. 52–67, 2002. DOI: 10.1109/MCS.2002.1004010.
    [60]
    L. N. De Castro, F. J. von Zuben. Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation, vol. 6, no. 3, pp. 239–251, 2002. DOI: 10.1109/TEVC.2002.1011539.
    [61]
    S. Wen, W. Zhou, J. Zhang, Y. Xiang, W. L. Zhou, W. J. Jia. Modeling propagation dynamics of social network worms. IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 8, pp. 1633–1643, 2013. DOI: 10.1109/TPDS.2012.250.
    [62]
    S. Noreen, S. Murtaza, M. Z. Shafiq, M. Farooq. Evolvable malware. In Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, ACM, Montreal, Canada, pp. 1569−1576, 2009. DOI: 10.1145/1569901.1570111.
    [63]
    D. Kempe, J. Kleinberg, É. Tardos. Maximizing the spread of influence through a social network. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Washington DC, USA, pp. 137−146, 2003. DOI: 10.1145/956750.956769.
    [64]
    P. Shakarian, A. Bhatnagar, A. Aleali, E. Shaabani, R. Guo. The independent cascade and linear threshold models. Diffusion in Social Networks, P. Shakarian, A. Bhatnagar, A. Aleali, E. Shaabani, R. C. Guo, Eds., Cham, Germany: Springer, pp. 35−48, 2015. DOI: 10.1007/978-3-319-23105-1_4.
    [65]
    S. Han, F. Z. Zhuang, Q. He, Z. Z. Shi, X. Ao. Energy model for rumor propagation on social networks. Physica A:Statistical Mechanics and its Applications, vol. 394, pp. 99–109, 2014. DOI: 10.1016/j.physa.2013.10.003.
    [66]
    V. Indu, S. M. Thampi. A nature-inspired approach based on forest fire model for modeling rumor propagation in social networks. Journal of Network and Computer Applications, vol. 125, pp. 28–41, 2019. DOI: 10.1016/j.jnca.2018.10.003.
    [67]
    P. Shakarian, V. S. Subrahmanian, M. L. Sapino. Using generalized annotated programs to solve social network optimization problems. In Proceedings of the 26th International Conference on Logic Programming, Edinburgh, UK, pp. 182−191, 2010. DOI: 10.4230/LIPIcs.ICLP.2010.182.
    [68]
    M. Broecheler, P. Shakarian, V. S. Subrahmanian. A scalable framework for modeling competitive diffusion in social networks. In Proceedings of the 2nd IEEE International Conference on Social Computing, IEEE, Minneapolis, USA, pp. 295−302, 2010. DOI: 10.1109/SocialCom.2010.49.
    [69]
    J. J. Binney. The Theory of Critical Phenomena: An Introduction to the Renormalization Group, Oxford, UK: Clarendon Press, 1992.
    [70]
    R. A. Holley, T. M. Liggett. Ergodic theorems for weakly interacting infinite systems and the voter model. The Annals of Probability, vol. 3, no. 4, pp. 643–663, 1975. DOI: 10.1214/aop/1176996306.
    [71]
    K. Sznajd-Weron, J. Sznajd. Opinion evolution in closed community. International Journal of Modern Physics C, vol. 11, no. 6, pp. 1157–1165, 2000. DOI: 10.1142/S0129183100000936.
    [72]
    P. L. Krapivsky, S. Redner. Dynamics of majority rule in two-state interacting spin systems. Physical Review Letters, vol. 90, no. 23, Article number 238701, 2003. DOI: 10.1103/PhysRevLett.90.238701.
    [73]
    G. Deffuant, D. Neau, F. Amblard, G. Weisbuch. Mixing beliefs among interacting agents. Advances in Complex Systems, vol. 3, no. 01n04, pp. 87–98, 2000. DOI: 10.1142/S0219525900000078.
    [74]
    R. Hegselmann, U. Krause. Truth and cognitive division of labour: First steps towards a computer aided social epistemology. Journal of Artificial Societies and Social Simulation, vol. 9, no. 3, Article number 10, 2006.
    [75]
    H. P. Young. Innovation diffusion in heterogeneous populations: Contagion, social influence, and social learning. American Economic Review, vol. 99, no. 5, pp. 1899–1924, 2009. DOI: 10.1257/aer.99.5.1899.
    [76]
    E. M. Rogers. Diffusion of Innovations, 4th ed., New York, USA: Simon and Schuster, 2010.
    [77]
    L. Molleman, P. van den Berg, F. J. Weissing. Consistent individual differences in human social learning strategies. Nature Communications, vol. 5, Article number 3570, 2014. DOI: 10.1038/ncomms4570.
    [78]
    H. P. Young. The evolution of social norms. Annual Review of Economics, vol. 7, pp. 359–387, 2015. DOI: 10.1146/annurev-economics-080614-115322.
    [79]
    F. M. Bass. A new product growth for model consumer durables. Management Science, vol. 15, no. 5, pp. 215–227, 1969. DOI: 10.1287/mnsc.15.5.215.
    [80]
    S. Morris. Contagion. The Review of Economic Studies, vol. 67, no. 1, pp. 57–78, 2000. DOI: 10.1111/1467-937X.00121.
    [81]
    D. Guilbeault, J. Becker, D. Centola. Complex contagions: A decade in review. Complex Spreading Phenomena in Social Systems, S. Lehmann Y. Y. Ahn, Eds., Cham, Germany: Springer, pp. 3−25, 2018. DOI: 10.1007/978-3-319-77332-2_1.
    [82]
    J. N. Yan, M. G. Gong, L. J. Ma, S. F. Wang, B. Shen. Structure optimization based on memetic algorithm for adjusting epidemic threshold on complex networks. Applied Soft Computing, vol. 49, pp. 224–237, 2016. DOI: 10.1016/j.asoc.2016.08.017.
    [83]
    M. J. Mahmoodabadi. Epidemic model analyzed via particle swarm optimization based homotopy perturbation method. Informatics in Medicine Unlocked, vol. 18, Article number 100293, 2020. DOI: 10.1016/j.imu.2020.100293.
    [84]
    C. Pizzuti, A. Socievole. Constrained evolutionary algorithms for epidemic spreading curing policy. Applied Soft Computing, vol. 90, Article number 106173, 2020. DOI: 10.1016/j.asoc.2020.106173.
    [85]
    S. F. Wang, M. G. Gong, W. F. Liu, Y. Wu. Preventing epidemic spreading in networks by community detection and memetic algorithm. Applied Soft Computing, vol. 89, Article number 106118, 2020. DOI: 10.1016/j.asoc.2020.106118.
    [86]
    F. Afifi, N. B. Anuar, S. Shamshirband, K. K. R. Choo. DyHAP: Dynamic hybrid ANFIS-PSO approach for predicting mobile malware. PLoS One, vol. 11, no. 9, Article number e0162627, 2016. DOI: 10.1371/journal.pone.0162627.
    [87]
    T. C. Wu, J. J. Wu, W. You. Optimizing robustness of complex networks with heterogeneous node functions based on the memetic algorithm. Physica A:Statistical Mechanics and its Applications, vol. 511, pp. 143–153, 2018. DOI: 10.1016/j.physa.2018.07.042.
    [88]
    M. X. Zhou, J. Liu. A two-phase multiobjective evolutionary algorithm for enhancing the robustness of scale-free networks against multiple malicious attacks. IEEE Transactions on Cybernetics, vol. 47, no. 2, pp. 539–552, 2017. DOI: 10.1109/TCYB.2016.2520477.
    [89]
    Y. Liu, X. Wang, J. Kurths. Framework of evolutionary algorithm for investigation of influential nodes in complex networks. IEEE Transactions on Evolutionary Computation, vol. 23, no. 6, pp. 1049–1063, 2019. DOI: 10.1109/TEVC.2019.2901012.
    [90]
    W. N. Chen, D. Z. Tan, Q. Yang, T. L. Gu, J. Zhang. Ant colony optimization for the control of pollutant spreading on social networks. IEEE Transactions on Cybernetics, vol. 50, no. 9, pp. 4053–4065, 2020. DOI: 10.1109/TCYB.2019.2922266.
    [91]
    P. Shah, Z. Kobti. Multimodal fake news detection using a Cultural Algorithm with situational and normative knowledge. In Proceedings of IEEE Congress on Evolutionary Computation, IEEE, Glasgow, UK, 2020. DOI: 10.1109/CEC48606.2020.9185643.
    [92]
    A. Tselykh, V. Vasilev, L. Tselykh. Management of control impacts based on maximizing the spread of influence. International Journal of Automation and Computing, vol. 16, no. 3, pp. 341–353, 2019. DOI: 10.1007/s11633-018-1167-2.
    [93]
    A. Goyal, F. Bonchi, L. V. S. Lakshmanan, S. Venkatasubramanian. On minimizing budget and time in influence propagation over social networks. Social Network Analysis and Mining, vol. 3, no. 2, pp. 179–192, 2013. DOI: 10.1007/s13278-012-0062-z.
    [94]
    M. G. Gong, C. Song, C. Duan, L. J. Ma, B. Shen. An efficient memetic algorithm for influence maximization in social networks. IEEE Computational Intelligence Magazine, vol. 11, no. 3, pp. 22–33, 2016. DOI: 10.1109/MCI.2016.2572538.
    [95]
    M. Weskida, R. Michalski. Evolutionary algorithm for seed selection in social influence process. In Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, IEEE, San Francisco, USA, pp. 1189−1196, 2016. DOI: 10.1109/ASONAM.2016.7752390.
    [96]
    D. Bucur, G. Iacca. Influence maximization in social networks with genetic algorithms. In Proceedings of the 19th European Conference on the Applications of Evolutionary Computation, Springer, Porto, Portugal, pp. 379−392, 2016. DOI: 10.1007/978-3-319-31204-0_25.
    [97]
    K. Q. Zhang, H. F. Du, M. W. Feldman. Maximizing influence in a social network: Improved results using a genetic algorithm. Physica A:Statistical Mechanics and its Applications, vol. 478, pp. 20–30, 2017. DOI: 10.1016/j.physa.2017.02.067.
    [98]
    M. Weskida, R. Michalski. Finding influentials in social networks using evolutionary algorithm. Journal of Computational Science, vol. 31, pp. 77–85, 2019. DOI: 10.1016/j.jocs.2018.12.010.
    [99]
    D. Li, C. H. Wang, S. P. Zhang, G. L. Zhou, D. H. Chu, C. Wu. Positive influence maximization in signed social networks based on simulated annealing. Neurocomputing, vol. 260, pp. 69–78, 2017. DOI: 10.1016/j.neucom.2017.03.003.
    [100]
    J. F. Robles, M. Chica, O. Cordon. Evolutionary multiobjective optimization to target social network influentials in viral marketing. Expert Systems with Applications, vol. 147, Article number 113183, 2020. DOI: 10.1016/j.eswa.2020.113183.
    [101]
    C. Salavati A. Abdollahpouri. Identifying influential nodes based on ant colony optimization to maximize profit in social networks. Swarm and Evolutionary Computation, vol. 51, Article number 100614, 2019. DOI: 10.1016/j.swevo.2019.100614.
    [102]
    F. Stonedahl, W. Rand, U. Wilensky. Evolving viral marketing strategies. In Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, ACM, Portland, USA, pp. 1195−1202, 2010. DOI: 10.1145/1830483.1830701.
    [103]
    R. Olivares, F. Muñoz, F. Riquelme. A multi-objective linear threshold influence spread model solved by swarm intelligence-based methods. Knowledge-Based Systems, vol. 212, Article number 106623, 2021. DOI: 10.1016/j.knosys.2020.106623.
    [104]
    D. Bucur, G. Iacca, A. Marcelli, G. Squillero, A. Tonda. Multi-objective evolutionary algorithms for influence maximization in social networks. In Proceedings of the 20th European Conference on the Applications of Evolutionary Computation, Springer, Amsterdam, The Netherlands, pp. 221−233, 2017. DOI: 10.1007/978-3-319-55849-3_15.
    [105]
    X. K. Wu, T. F. Zhao. Application of natural language processing in social communication: A review and future perspectives. Computer Science, vol. 47, no. 6, pp. 184–193, 2020. DOI: 10.11896/jsjkx.191200151. (in Chinese)
    [106]
    J. Ma, W. Gao, Z. Y. Wei, Y. M. Lu, K. F. Wong. Detect rumors using time series of social context information on microblogging websites. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, ACM, Melbourne, Australia, pp. 1751−1754, 2015. DOI: 10.1145/2806416.2806607.
    [107]
    Z. Zhao, P. Resnick, Q. Z. Mei. Enquiring minds: Early detection of rumors in social media from enquiry posts. In Proceedings of the 24th International Conference on World Wide Web, ACM, Florence, Italy, pp. 1395−1405, 2015. DOI: 10.1145/2736277.2741637.
    [108]
    R. Sicilia, S. Lo Giudice, Y. L. Pei, M. Pechenizkiy, P. Soda. Twitter rumour detection in the health domain. Expert Systems with Applications, vol. 110, pp. 33–40, 2018. DOI: 10.1016/j.eswa.2018.05.019.
    [109]
    P. Meel, D. K. Vishwakarma. Fake news, rumor, information pollution in social media and web: A contemporary survey of state-of-the-arts, challenges and opportunities. Expert Systems with Applications, vol. 153, Article number 112986, 2020. DOI: 10.1016/j.eswa.2019.112986.
    [110]
    B. Chang, T. Xu, Q. Liu, E. H. Chen. Study on information diffusion analysis in social networks and its applications. International Journal of Automation and Computing, vol. 15, no. 4, pp. 377–401, 2018. DOI: 10.1007/s11633-018-1124-0.
    [111]
    S. Shelke, V. Attar. Source detection of rumor in social network – A review. Online Social Networks and Media, vol. 9, pp. 30–42, 2019. DOI: 10.1016/j.osnem.2018.12.001.
    [112]
    X. K. Wu, T. F. Zhao, W. N. Chen, J. Zhang. Toward predicting active participants in tweet streams: A case study on two civil rights events. IEEE Transactions on Knowledge and Data Engineering, to be published. DOI: 10.1109/TKDE.2020.3017635.
    [113]
    D. Shah, T. Zaman. Rumors in a network: Who′s the culprit? IEEE Transactions on Information Theory, vol. 57, no. 8, pp. 5163–5181, 2011. DOI: 10.1109/TIT.2011.2158885.
    [114]
    D. Shah, T. Zaman. Rumor centrality: A universal source detector. In Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems, ACM, London, UK, pp. 199−210, 2012. DOI: 10.1145/2254756.2254782.
    [115]
    W. Q. Luo, W. P. Tay, M. Leng. Identifying infection sources and regions in large networks. IEEE Transactions on Signal Processing, vol. 61, no. 11, pp. 2850–2865, 2013. DOI: 10.1109/TSP.2013.2256902.
    [116]
    G. Mahinthakumar, M. Sayeed. Hybrid genetic algorithm − Local search methods for solving groundwater source identification inverse problems. Journal of Water Resources Planning and Management, vol. 131, no. 1, pp. 45–57, 2005. DOI: 10.1061/(ASCE)0733-9496(2005)131:1(45).
    [117]
    L. Liu, E. M. Zechman, E. D. Brill Jr, G. Mahinthakumar. Adaptive contamination source identification in water distribution systems using an evolutionary algorithm-based dynamic optimization procedure. In Proceedings of the 8th Annual Water Distribution Systems Analysis Symposium, pp. 1−9, 2008.
    [118]
    M. L. Zierolf, M. M. Polycarpou, J. G. Uber. Development and autocalibration of an input-output model of chlorine transport in drinking water distribution systems. IEEE Transactions on Control Systems Technology, vol. 6, no. 4, pp. 543–553, 1998. DOI: 10.1109/87.701351.
    [119]
    F. Shang, J. G. Uber, M. M. Polycarpou. Particle backtracking algorithm for water distribution system analysis. Journal of Environmental Engineering, vol. 128, no. 5, pp. 441–450, 2002. DOI: 10.1061/(ASCE)0733-9372(2002)128:5(441).
    [120]
    C. D. Laird, L. T. Biegler, B. G. van Bloemen Waanders, R. A. Bartlett. Contamination source determination for water networks. Journal of Water Resources Planning and Management, vol. 131, no. 2, pp. 125–134, 2005. DOI: 10.1061/(ASCE)0733-9496(2005)131:2(125).
    [121]
    C. M. Chen, D. Hicks. Tracing knowledge diffusion. Scientometrics, vol. 59, no. 2, pp. 199–211, 2004. DOI: 10.1023/B:SCIE.0000018528.59913.48.
    [122]
    A. J. Nelson. Measuring knowledge spillovers: What patents, licenses and publications reveal about innovation diffusion. Research Policy, vol. 38, no. 6, pp. 994–1005, 2009. DOI: 10.1016/j.respol.2009.01.023.
    [123]
    L. Y. Y. Lu, J. S. Liu. An innovative approach to identify the knowledge diffusion path: The case of resource-based theory. Scientometrics, vol. 94, no. 1, pp. 225–246, 2013. DOI: 10.1007/s11192-012-0744-3.
    [124]
    Y. Xiao, L. Y. Y. Lu, J. S. Liu, Z. L. Zhou. Knowledge diffusion path analysis of data quality literature: A main path analysis. Journal of Informetrics, vol. 8, no. 3, pp. 594–605, 2014. DOI: 10.1016/j.joi.2014.05.001.
    [125]
    W. J. Hu, K. K. Singh, F. Y. Xiao, J. Han, C. N. Chuah, Y. J. Lee. Who will share my image?: Predicting the content diffusion path in online social networks. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining, ACM, Marina Del Rey, USA, pp. 252−260, 2018. DOI: 10.1145/3159652.3159705.
    [126]
    B. Wierzba, W. J. Nowak. The diffusion path model in a ternary multiphase system. Physica A:Statistical Mechanics and its Applications, vol. 509, pp. 265–274, 2018. DOI: 10.1016/j.physa.2018.06.020.
    [127]
    X. Yang, X. Z. Chen, J. Ma, S. H. Li. An information diffusion path construction algorithm based on user characteristics and text characteristics. In Proceedings International Conference on Machine Learning and Cybernetics, IEEE, Kobe, Japan, 2019. DOI: 10.1109/ICMLC48188.2019.8949204.
    [128]
    Y. M. Li, C. Y. Lai, L. F. Lin. A diffusion planning mechanism for social marketing. Information &Management, vol. 54, no. 5, pp. 638–650, 2017. DOI: 10.1016/j.im.2016.12.006.
    [129]
    L. J. Abu-Raddad, B. I. S. van der Ventel, N. M. Ferguson. Interactions of multiple strain pathogen diseases in the presence of coinfection, cross immunity, and arbitrary strain diversity. Physical Review Letters, vol. 100, no. 16, Article number 168102, 2008. DOI: 10.1103/PhysRevLett.100.168102.
    [130]
    Q. H. Liu, L. F. Zhong, W. Wang, T. Zhou, H. E. Stanley. Interactive social contagions and co-infections on complex networks. Chaos:An Interdisciplinary Journal of Nonlinear Science, vol. 28, no. 1, Article number 013120, 2018. DOI: 10.1063/1.5010002.
    [131]
    X. Y. Wang, T. F. Zhao. Model for multi-messages spreading over complex networks considering the relationship between messages. Communications in Nonlinear Science and Numerical Simulation, vol. 48, pp. 63–69, 2017. DOI: 10.1016/j.cnsns.2016.12.019.
    [132]
    S. Funk, E. Gilad, C. Watkins, V. A. A. Jansen. The spread of awareness and its impact on epidemic outbreaks. Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 16, pp. 6872–6877, 2009. DOI: 10.1073/pnas.0810762106.
    [133]
    C. Granell, S. Gómez, A. Arenas. Dynamical interplay between awareness and epidemic spreading in multiplex networks. Physical Review Letters, vol. 111, no. 12, Article number 128701, 2013. DOI: 10.1103/PhysRevLett.111.128701.
    [134]
    W. Wang, Q. H. Liu, J. H. Liang, Y. Q. Hu, T. Zhou. Coevolution spreading in complex networks. Physics Reports, vol. 820, pp. 1–51, 2019. DOI: 10.1016/j.physrep.2019.07.001.
    [135]
    X. Liu, M. Li, S. S. Li, S. L. Peng, X. K. Liao, X. P. Lu. IMGPU: GPU-accelerated influence maximization in large-scale social networks. IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 1, pp. 136–145, 2014. DOI: 10.1109/TPDS.2013.41.
    [136]
    L. de P. Veronese, R. A. Krohling. Differential evolution algorithm on the GPU with C-CUDA. In Proceedings of IEEE Congress on Evolutionary Computation, IEEE, Barcelona, Spain, 2010. DOI: 10.1109/CEC.2010.5586219.
    [137]
    J. R. Cheng, M. Gen. Accelerating genetic algorithms with GPU computing: A selective overview. Computers &Industrial Engineering, vol. 128, pp. 514–525, 2019. DOI: 10.1016/j.cie.2018.12.067.
    [138]
    Y. G. Woldesenbet, G. G. Yen. Dynamic evolutionary algorithm with variable relocation. IEEE Transactions on Evolutionary Computation, vol. 13, no. 3, pp. 500–513, 2009. DOI: 10.1109/TEVC.2008.2009031.
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