Abstract
Recognizing crucial seed spreaders of complex networks is an open issue that studies the dynamic spreading process and analyzes the performance of networks. However, most of the findings design the hierarchical model based on nodes’ degree such as Kshell decomposition for obtaining global information, and identifying effects brought by the weight value of each layer is coarse. In addition, local structural information fails to be effectively captured when neighborhood nodes are sometimes unconnected in the hierarchical structure. To solve these issues, in this paper, we design a novel hierarchical structure based on the shortest path distance by using the interpretative structure model and determine influence weights of each layer. Furthermore, we also design the local neighborhood overlap coefficient and the local index based on the overlap (LIO) by considering two conditions of connected and unconnected neighborhood nodes in the hierarchical structure. For reaching a comprehensive recognition and finding crucial seed spreaders precisely, we introduce influence weights vector, local evaluation index matrix after normalization and the weight vector of local indexes into a new hybrid recognition framework. The proposed method adopts a series of indicators, including the monotonicity relation, Susceptible-Infected-Susceptible model, complementary cumulative distribution function, Kendall’s coefficient, spreading scale ratio and average shortest path length, to execute corresponding experiments and evaluate the diffusion ability in different datasets. Results demonstrate that, our method outperforms involved algorithms in the recognition effects and spreading capability.
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References
Arndt, S., Turvey, C., & Andreasen, N. C. (1999). Correlating and predicting psychiatric symptom ratings: Spearmans r versus kendalls tau correlation. Journal of Psychiatric Research, 33(2), 97–104. https://doi.org/10.1016/S0022-3956(98)90046-2
Auer S, Bizer C, & Kobilarov G, et al. (2007). Dbpedia: A nucleus for a web of open data. In: International Semantic Web Conference, (pp. 722–735). Springer https://doi.org/10.1007/978-3-540-76298-0_52
Bae, J., & Kim, S. (2014). Identifying and ranking influential spreaders in complex networks by neighborhood coreness. Physica A: Statistical Mechanics and its Applications, 395, 549–559. https://doi.org/10.1016/j.physa.2013.10.047
Beuming, T., Skrabanek, L., Niv, M. Y., et al. (2005). Pdzbase: a protein-protein interaction database for pdz-domains. Bioinformatics, 21(6), 827–828. https://doi.org/10.1093/bioinformatics/bti098
Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120. https://doi.org/10.1080/0022250X.1972.9989806
Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30(1–7), 107–117. https://doi.org/10.1016/S0169-7552(98)00110-X
Castellano, C., & Pastor-Satorras, R. (2010). Thresholds for epidemic spreading in networks. Physical Review Letters, 105(21), 218701. https://doi.org/10.1103/PhysRevLett.105.218701
Chen, D., Lü, L., Shang, M. S., et al. (2012). Identifying influential nodes in complex networks. Physica A: Statistical Mechanics and its Applications, 391(4), 1777–1787. https://doi.org/10.1016/j.physa.2011.09.017
Chen, D. B., Xiao, R., Zeng, A., et al. (2014). Path diversity improves the identification of influential spreaders. Europhysics Letters, 104(6), 68006. https://doi.org/10.1209/0295-5075/104/68006
Cohen, J. E. (1992). Infectious diseases of humans: dynamics and control. JAMA, 268(23), 3381–3381. https://doi.org/10.1001/jama.1992.03490230111047
Fu, Y. H., Huang, C. Y., & Sun, C. T. (2015). Using global diversity and local topology features to identify influential network spreaders. Physica A: Statistical Mechanics and its Applications, 433, 344–355. https://doi.org/10.1016/j.physa.2015.03.042
Gupta, A., Khatri, I., & Choudhry, A., et al. (2023). MCD: A modified community diversity approach for detecting influential nodes in social networks. Journal of Intelligent Information Systems, 1–23. https://doi.org/10.1007/s10844-023-00776-2
Haraldsdottir, S., Gupta, S., & Anderson, R. M. (1992). Preliminary studies of sexual networks in a male homosexual community in iceland. JAIDS Journal of Acquired Immune Deficiency Syndromes, 5(4), 374–381.
Kitsak, M., Gallos, L. K., Havlin, S., et al. (2010). Identification of influential spreaders in complex networks. Nature Physics, 6(11), 888–893. https://doi.org/10.1038/nphys1746
Kunegis, J. (2013). Konect: the koblenz network collection. In: Proceedings of the 22nd international conference on world wide web, (pp. 1343–1350). https://doi.org/10.1145/2487788.2488173
Laassem, B., Idarrou, A., Boujlaleb, L., et al. (2022). Label propagation algorithm for community detection based on coulomb’s law. Physica A: Statistical Mechanics and its Applications, 593, 126881. https://doi.org/10.1016/j.physa.2022.126881
Lei, M., & Cheong, K. H. (2022). Node influence ranking in complex networks: A local structure entropy approach. Chaos, Solitons & Fractals, 160, 112136. https://doi.org/10.1016/j.chaos.2022.112136
Leskovec, J., Kleinberg, J., & Faloutsos, C. (2007). Graph evolution: Densification and shrinking diameters. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(1), 2–es. https://doi.org/10.1145/1217299.1217301
Li, K., Gong, X., Guan, S., et al. (2012). Efficient algorithm based on neighborhood overlap for community identification in complex networks. Physica A: Statistical Mechanics and its Applications, 391(4), 1788–1796. https://doi.org/10.1016/j.physa.2011.09.027
Li, S., & Xiao, F. (2021). The identification of crucial spreaders in complex networks by effective gravity model. Information Sciences, 578, 725–749. https://doi.org/10.1016/j.ins.2021.08.026
Liu, X., Ye, S., Fiumara, G., et al. (2022). Influential spreaders identification in complex networks with topsis and k-shell decomposition. IEEE Transactions on Computational Social Systems, 10(1), 347–361. https://doi.org/10.1109/TCSS.2022.3148778
Liu, Z., Jiang, C., Wang, J., et al. (2015). The node importance in actual complex networks based on a multi-attribute ranking method. Knowledge-Based Systems, 84, 56–66. https://doi.org/10.1016/j.knosys.2015.03.026
Magal, P., & Webb, G. (2018). The parameter identification problem for sir epidemic models: identifying unreported cases. Journal of Mathematical Biology, 77, 1629–1648. https://doi.org/10.1007/s00285-017-1203-9
Mandal, A., & Deshmukh, S. (1994). Vendor selection using interpretive structural modelling (ism). International Journal of Operations & Production Management, 14(6), 52–59. https://doi.org/10.1108/01443579410062086
Namtirtha, A., Dutta, A., & Dutta, B. (2018). Identifying influential spreaders in complex networks based on kshell hybrid method. Physica A: Statistical Mechanics and its Applications, 499, 310–324. https://doi.org/10.1016/j.physa.2018.02.016
Namtirtha, A., Dutta, A., & Dutta, B. (2020). Weighted kshell degree neighborhood: A new method for identifying the influential spreaders from a variety of complex network connectivity structures. Expert Systems with Applications, 139, 112859. https://doi.org/10.1016/j.eswa.2019.112859
Namtirtha, A., Dutta, B., & Dutta, A. (2022). Semi-global triangular centrality measure for identifying the influential spreaders from undirected complex networks. Expert Systems with Applications, 206, 117791. https://doi.org/10.1016/j.eswa.2022.117791
Rozemberczki, B., & Sarkar, R. (2020). Characteristic functions on graphs: Birds of a feather, from statistical descriptors to parametric models. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, (pp. 1325–1334). https://doi.org/10.1145/3340531.3411866
Shareef, M. A., Mukerji, B., Dwivedi, Y. K., et al. (2019). Social media marketing: Comparative effect of advertisement sources. Journal of Retailing and Consumer Services, 46, 58–69. https://doi.org/10.1016/j.jretconser.2017.11.001
Sheikhahmadi, A., & Nematbakhsh, M. A. (2017). Identification of multi-spreader users in social networks for viral marketing. Journal of Information Science, 43(3), 412–423. https://doi.org/10.1177/0165551516644171
Strait, B. J., & Dewey, T. G. (1996). The shannon information entropy of protein sequences. Biophysical Journal, 71(1), 148–155. https://doi.org/10.1016/S0006-3495(96)79210-X
Stumpf, M. P., Wiuf, C., & May, R. M. (2005). Subnets of scale-free networks are not scale-free: sampling properties of networks. Proceedings of the National Academy of Sciences, 102(12), 4221–4224. https://doi.org/10.1073/pnas.0501179102
Tong, T., Dong, Q., Sun, J., et al. (2023). Vital spreaders identification synthesizing cross entropy and information entropy with kshell method. Expert Systems with Applications, 224, 119928. https://doi.org/10.1016/j.eswa.2023.119928
Tong, T. C., Jiang, Y., Zhou, Y., et al. (2019). Mitigation strategy for the cascading failure of complex networks based on node capacity control function. IEEE Access, 7, 184743–184758. https://doi.org/10.1109/ACCESS.2019.2959122
Ullah, A., Wang, B., Sheng, J., et al. (2021). Identifying vital nodes from local and global perspectives in complex networks. Expert Systems with Applications, 186, 115778. https://doi.org/10.1016/j.eswa.2021.115778
Ullah, A., Wang, B., Sheng, J., et al. (2022). Escape velocity centrality: escape influence-based key nodes identification in complex networks. Applied Intelligence, 52(14), 16586–16604. https://doi.org/10.1007/s10489-022-03262-4
Ullah, A., Shao, J., Yang, Q., et al. (2023a). Lss: A locality-based structure system to evaluate the spreader’s importance in social complex networks. Expert Systems with Applications, 228, 120326. https://doi.org/10.1016/j.eswa.2023.120326
Ullah, A., Sheng, J., Wang, B., et al. (2023b). Leveraging neighborhood and path information for influential spreaders recognition in complex networks. Journal of Intelligent Information Systems, (pp. 1–25). https://doi.org/10.1007/s10844-023-00822-z
Wan, Y. P., Wang, J., Zhang, D. G., et al. (2018). Ranking the spreading capability of nodes in complex networks based on link significance. Physica A: Statistical Mechanics and its Applications, 503, 929–937. https://doi.org/10.1016/j.physa.2018.08.127
Wang, M., Li, W., Guo, Y., et al. (2020a). Identifying influential spreaders in complex networks based on improved k-shell method. Physica A: Statistical Mechanics and its Applications, 554, 124229. https://doi.org/10.1016/j.physa.2020.124229
Wang, T., Chen, S., Wang, X., et al. (2020b). Label propagation algorithm based on node importance. Physica A: Statistical Mechanics and its Applications, 551, 124137. https://doi.org/10.1016/j.physa.2020.124137
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of small-world networks. Nature, 393(6684), 440–442. https://doi.org/10.1038/30918
Xiao, F., Li, J., & Wei, B. (2022). Cascading failure analysis and critical node identification in complex networks. Physica A: Statistical Mechanics and its Applications, 596, 127117. https://doi.org/10.1016/j.physa.2022.127117
Xu, G., & Meng, L. (2023). A novel algorithm for identifying influential nodes in complex networks based on local propagation probability model. Chaos, Solitons & Fractals, 168, 113155. https://doi.org/10.1016/j.chaos.2023.113155
Xu, S., Teng, C., Zhou, Y., et al. (2019). Identifying the diffusion source in complex networks with limited observers. Physica A: Statistical Mechanics and its Applications, 527, 121267. https://doi.org/10.1016/j.physa.2019.121267
Yang, F., Zhang, R., Yang, Z., et al. (2017). Identifying the most influential spreaders in complex networks by an extended local k-shell sum. International Journal of Modern Physics C, 28(01), 1750014. https://doi.org/10.1142/S0129183117500140
Zeng, A., & Zhang, C. J. (2013). Ranking spreaders by decomposing complex networks. Physics Letters A, 377(14), 1031–1035. https://doi.org/10.1016/j.physleta.2013.02.039
Zhang, J. X., Chen, D. B., Dong, Q., et al. (2016). Identifying a set of influential spreaders in complex networks. Scientific Reports, 6(1), 27823. https://doi.org/10.1038/srep27823
Zhang, X., Li, Z., Qian, K., et al. (2020). Influential node identification in a constrained greedy way. Physica A: Statistical Mechanics and its Applications, 557, 124887. https://doi.org/10.1016/j.physa.2020.124887
Zhang, Y., Su, Y., Weigang, L., et al. (2018). Identifying multiple influential spreaders with local relative weakening effect in complex networks. Europhysics Letters, 124(2), 28001. https://doi.org/10.1209/0295-5075/124/28001
Acknowledgements
We would like to thank Jinsheng Sun for his help in providing the experimental platform.
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This work was supported by National Natural Science Foundation of China (Grant number: 52177090); Jiangxi Provincial Natural Science Foundation of China(Grant number: 20224BAB202028); and Postgraduate Research & Practice Innovation Program of Jiangsu Province of China (Grant number: KYCX23_0476).
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A. wrote the main manuscript text and conducted experiments. B. , C. and D. wrote a part of manuscript text and prepared all of figures. E. and F. reviewed the manuscript.
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Tong, T., Wang, M., Yuan, W. et al. A hybrid recognition framework of crucial seed spreaders in complex networks with neighborhood overlap. J Intell Inf Syst (2024). https://doi.org/10.1007/s10844-024-00849-w
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DOI: https://doi.org/10.1007/s10844-024-00849-w