341
Views
0
CrossRef citations to date
0
Altmetric
Research Article

A multi-factorial evolutionary algorithm concerning diversity information for solving the multitasking Robust Influence Maximization Problem on networks

ORCID Icon, ORCID Icon & ORCID Icon
Article: 2275534 | Received 13 Jun 2023, Accepted 21 Oct 2023, Published online: 16 Nov 2023

References

  • Bai, M., Tan, Y., Wang, X., Zhu, B., & Li, G. (2021). Optimized algorithm for skyline community discovery in multi-valued networks. IEEE Access, 9, 37574–37589. https://doi.org/10.1109/ACCESS.2021.3063317
  • Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512. https://doi.org/10.1126/science.286.5439.509
  • Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D., Marlow, C., Settle, J. E., & Fowler, J. H. (2012). A 61-million-person experiment in social influence and political mobilization. Nature, 489(7415), 295–298. https://doi.org/10.1038/nature11421
  • Borgs, C., Brautbar, M., Chayes, J., & Lucier, B. (2014). Maximizing social influence in nearly optimal time. Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms.
  • Buesser, P., Daolio, F., & Tomassini, M. (2011). Optimizing the robustness of scale-free networks with simulated annealing. International Conference on Adaptive and Natural Computing Algorithms.
  • Chen, W., Lin, T., Tan, Z., Zhao, M., & Zhou, X. (2016). Robust influence maximization. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
  • Chen, W., Wang, Y., & Yang, S. (2009). Efficient influence maximization in social networks. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/1599272
  • Da, B., Gupta, A., & Ong, Y. S. (2019). Curbing negative influences online for seamless transfer evolutionary optimization. IEEE Transactions on Cybernetics, 49(12), 4365–4378. https://doi.org/10.1109/TCYB.2018.2864345
  • Erds, P., & Rényi, A. (1961). On the evolution of random graphs. Publication of the Mathematical Institute of the Hungarian Academy of Sciences.
  • Farid, A. M. (2015). Symmetrica: Test case for transportation electrification research. Infrastructure Complexity, 2(1), 1–10. https://doi.org/10.1186/s40551-015-0012-9
  • Gong, M., Song, C., Duan, C., Ma, L., & Shen, B. (2016). An efficient memetic algorithm for influence maximization in social networks. IEEE Computational Intelligence Magazine, 11(3), 22–33. https://doi.org/10.1109/MCI.2016.2572538
  • Gong, M., Tang, Z., Li, H., & Zhang, J. (2019). Evolutionary multitasking with dynamic resource allocating strategy. IEEE Transactions on Evolutionary Computation, 23(5), 858–869. https://doi.org/10.1109/TEVC.2019.2893614
  • Gong, M., Yan, J., Shen, B., Ma, L., & Cai, Q. (2016). Influence maximization in social networks based on discrete particle swarm optimization. Information Sciences, 367-368, 600–614. https://doi.org/10.1016/j.ins.2016.07.012
  • Gong, Y., Liu, S., & Bai, Y. (2021). Efficient parallel computing on the game theory-aware robust influence maximization problem. Knowledge-Based Systems, 220, 106942. https://doi.org/10.1016/j.knosys.2021.106942
  • Goyal, A., Lu, W., & Lakshmanan, L. V. (2011). Celf++ optimizing the greedy algorithm for influence maximization in social networks. Proceedings of the 20th International Conference Companion on World Wide Web.
  • Gupta, A., Ong, Y.-S., & Feng, L. (2015). Multifactorial evolution: Toward evolutionary multitasking. IEEE Transactions on Evolutionary Computation, 20(3), 343–357. https://doi.org/10.1109/TEVC.2015.2458037
  • Gupta, A., Ong, Y.-S., Feng, L., & Tan, K. C. (2016). Multiobjective multifactorial optimization in evolutionary multitasking. IEEE Transactions on Cybernetics, 47(7), 1652–1665. https://doi.org/10.1109/TCYB.2016.2554622
  • Gupta, A., Zhou, L., Ong, Y.-S., Chen, Z., & Hou, Y. (2022). Half a dozen real-world applications of evolutionary multitasking, and more. IEEE Computational Intelligence Magazine, 17(2), 49–66. https://doi.org/10.1109/MCI.2022.3155332
  • Huang, D., Tan, X., Chen, N., & Fan, Z. (2022). A memetic algorithm for solving the robust influence maximization problem on complex networks against structural failures. Sensors, 22(6), 2191. https://doi.org/10.3390/s22062191
  • Karafotias, G., Hoogendoorn, M., & Eiben, ÁE. (2014). Parameter control in evolutionary algorithms: Trends and challenges. IEEE Transactions on Evolutionary Computation, 19(2), 167–187. https://doi.org/10.1109/TEVC.2014.2308294
  • Kempe, D., Kleinberg, J., & Tardos, É. (2003). Maximizing the spread of influence through a social network. Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
  • Kurant, M., & Thiran, P. (2006). Layered complex networks. Physical Review Letters, 96(13), 138701. https://doi.org/10.1103/PhysRevLett.96.138701
  • Lee, J.-R., & Chung, C.-W. (2014). A fast approximation for influence maximization in large social networks. Proceedings of the 23rd International Conference on World Wide Web.
  • Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., & Glance, N. (2007). Cost-effective outbreak detection in networks. Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
  • Li, Q., Cheng, L., Wang, W., Li, X., Li, S., & Zhu, P. (2023). Influence maximization through exploring structural information. Applied Mathematics and Computation, 442, 127721. https://doi.org/10.1016/j.amc.2022.127721
  • Liaw, R. T., & Ting, C. K. (2019). Evolutionary manytasking optimization based on symbiosis in biocoenosis. Proceedings of the AAAI Conference on Artifical Intelligence.
  • Qiu, L., Jia, W., & Fan, X. (2019). Influence maximization algorithm based on overlapping community. Data Analysis Knowledge Discovery, 3(7), 94–102.
  • Rahimkhani, K., Aleahmad, A., Rahgozar, M., & Moeini, A. (2015). A fast algorithm for finding most influential people based on the linear threshold model. Expert Systems with Applications, 42(3), 1353–1361. https://doi.org/10.1016/j.eswa.2014.09.037
  • Ren, Z. M., Shao, F., Liu, J. G., Guo, Q., & Wang, B. H. (2013). Node importance measurement based on the degree and clustering coefficient information. ActaPhysica Sinica, 62(12), 522–526.
  • Samir, A. M., Rady, S., & Gharib, T. F. (2021). LKG: A fast scalable community-based approach for influence maximization problem in social networks. Physica A: Statistical Mechanics and its Applications, 582, 126258. https://doi.org/10.1016/j.physa.2021.126258
  • Schneider, C. M., Moreira, A. A., Andrade, J. S., Havlin, S., & Herrmann, H. J. (2011). Mitigation of malicious attacks on networks. Proceedings of the National Academy of Sciences, 108(10), 3838–3841. https://doi.org/10.1073/pnas.1009440108
  • Shen, C., & Zhang, K. (2022). Two-stage improved Grey Wolf optimization algorithm for feature selection on high-dimensional classification. Complex Intelligent Systems, 1–21.
  • Shen, F., Liu, J., & Wu, K. (2020). Evolutionary multitasking fuzzy cognitive map learning. Knowledge-Based Systems, 192, 105294. https://doi.org/10.1016/j.knosys.2019.105294
  • Srinivas, N., & Deb, K. (1994). Multiobjective function optimization using nondominated sorting genetic algorithms. Evolutionary Computation, 2(3), 1301–1308. https://doi.org/10.1162/evco.1994.2.3.221
  • Strogatz, S. H. (2001). Exploring complex networks. Nature, 410(6825), 268–276. https://doi.org/10.1038/35065725
  • Tang, Y., Shi, Y., & Xiao, X. (2015). Influence maximization in near-linear time: A martingale approach. Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data.
  • Tang, Y., Xiao, X., & Shi, Y. (2014). Influence maximization: Near-optimal time complexity meets practical efficiency. Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data.
  • Wang, C., Liu, J., Wu, K., & Wu, Z. (2021). Solving multitask optimization problems with adaptive knowledge transfer via anomaly detection. IEEE Transactions on Evolutionary Computation, 26(2), 304–318. https://doi.org/10.1109/TEVC.2021.3068157
  • Wang, S., Jin, Y., & Cai, M. (2023). Enhancing the robustness of networks against multiple damage models using a multifactorial evolutionary algorithm. IEEE Transactions on Systems, Man, Cybernetics: Systems.
  • Wang, S., & Liu, J. (2017). A multi-objective evolutionary algorithm for promoting the emergence of cooperation and controllable robustness on directed networks. IEEE Transactions on Network Science and Engineering, 5(2), 92–100. https://doi.org/10.1109/TNSE.2017.2742522
  • Wang, S., & Liu, J. (2019). Designing comprehensively robust networks against intentional attacks and cascading failures. Information Sciences, 478, 125–140. https://doi.org/10.1016/j.ins.2018.11.005
  • Wang, S., & Liu, J. (2021). A memetic algorithm for solving the robust influence maximization problem towards network structural perturbances. Chinese Journal of Computers, 44(6), 1153–1167.
  • Wang, S., Liu, J., & Jin, Y. (2019). Finding influential nodes in multiplex networks using a memetic algorithm. IEEE Transactions on Cybernetics, 51(2), 900–912. https://doi.org/10.1109/TCYB.2019.2917059
  • Wang, S., & Tan, X. (2022a). Determining seeds with robust influential ability from multi-layer networks: A multi-factorial evolutionary approach. Knowledge-Based Systems, 246, 108697. https://doi.org/10.1016/j.knosys.2022.108697
  • Wang, S., & Tan, X. (2022b). Solving the robust influence maximization problem on multi-layer networks via a Memetic algorithm. Applied Soft Computing, 121, 108750. https://doi.org/10.1016/j.asoc.2022.108750
  • Wang, Y., Cong, G., Song, G., & Xie, K. (2010). Community-based greedy algorithm for mining top-k influential nodes in mobile social networks. Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
  • Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440–442. https://doi.org/10.1038/30918
  • Wei, T., Wang, S., Zhong, J., Liu, D., & Zhang, J. (2021). A review on evolutionary multitask optimization: Trends and challenges. IEEE Transactions on Evolutionary Computation, 26(5), 941–960. https://doi.org/10.1109/TEVC.2021.3139437
  • Wu, K., Wang, C., & Liu, J. (2021). Evolutionary multitasking multilayer network reconstruction. IEEE Transactions on Cybernetics, 52(12), 12854–12868. https://doi.org/10.1109/TCYB.2021.3090769
  • Wu, Y., Ding, H., Xiang, B., Sheng, J., Ma, W., Qin, K., … Gong, M. (2023). Evolutionary multitask optimization in real-world applications: A survey. Journal of Artificial Intelligence Technology, 3(1), 32–38.
  • Xianli, Z., Jianxin, T., & Laicheng, C. (2020). Influence maximization algorithm based on reverse PageRank. Journal of Computer Applications, 40(1), 96.
  • Yang, J., & Liu, J. (2017). Influence maximization-cost minimization in social networks based on a multiobjective discrete particle swarm optimization algorithm. IEEE Access, 6, 2320–2329. https://doi.org/10.1109/ACCESS.2017.2782814
  • Zeng, A., & Liu, W. (2012). Enhancing network robustness against malicious attacks. Physical Review E, 85(6), 066130. https://doi.org/10.1103/PhysRevE.85.066130
  • Zhang, H., Nguyen, D. T., Zhang, H., & Thai, M. T. (2015). Least cost influence maximization across multiple social networks. IEEE/ACM Transactions on Networking, 24(2), 929–939. https://doi.org/10.1109/TNET.2015.2394793
  • Zheng, X., Qin, A. K., Gong, M., & Zhou, D. (2019). Self-regulated evolutionary multitask optimization. IEEE Transactions on Evolutionary Computation, 24(1), 16–28. https://doi.org/10.1109/TEVC.2019.2904696
  • Zhu, J., Ghosh, S., & Wu, W. (2019). Group influence maximization problem in social networks. IEEE Transactions on Computational Social Systems, 6(6), 1156–1164. https://doi.org/10.1109/TCSS.2019.2938575
  • Zhu, Q., Yang, C., Xu, Y., Wang, H., Zhang, C., & Han, J. (2021). Transfer learning of graph neural networks with ego-graph information maximization. Advances in Neural Information Processing Systems, 34, 1766–1779.