{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T04:20:06Z","timestamp":1773116406051,"version":"3.50.1"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T00:00:00Z","timestamp":1769212800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T00:00:00Z","timestamp":1773014400000},"content-version":"vor","delay-in-days":44,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s44443-025-00436-1","type":"journal-article","created":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T10:54:21Z","timestamp":1769252061000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Competitive influence maximization in biased community networks: An adaptive algorithmic approach"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6493-2778","authenticated-orcid":false,"given":"YuLe","family":"Zou","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,24]]},"reference":[{"key":"436_CR1","doi-asserted-by":"crossref","unstructured":"Agarwal S,\u00a0Mehta S (2018) Social influence maximization using genetic algorithm with dynamic probabilities, in: 2018 Eleventh International Conference on Contemporary Computing (IC3), IEEE, pp. 1\u20136","DOI":"10.1109\/IC3.2018.8530626"},{"key":"436_CR2","doi-asserted-by":"crossref","unstructured":"Ali K, Wang C-Y, Yeh M-Y, Li C-T, Chen Y-S (2021a) Nedrl-cim: Network embedding meets deep reinforcement learning to tackle competitive influence maximization on evolving social networks. In: 2021 IEEE 8th international conference on data science and advanced analytics (DSAA). IEEE:1\u20139","DOI":"10.1109\/DSAA53316.2021.9564111"},{"key":"436_CR3","doi-asserted-by":"crossref","unstructured":"Ali K, Wang C-Y, Yeh M-Y, Li C-T, Chen Y-S (2021b) Nedrl-cim: Network embedding meets deep reinforcement learning to tackle competitive influence maximization on evolving social networks. In: 2021 IEEE 8th international conference on data science and advanced analytics (DSAA). IEEE:1\u20139","DOI":"10.1109\/DSAA53316.2021.9564111"},{"key":"436_CR4","doi-asserted-by":"publisher","first-page":"2439","DOI":"10.1016\/S0305-0548(03)00197-7","volume":"31","author":"N Azizi","year":"2004","unstructured":"Azizi N, Zolfaghari S (2004) Adaptive temperature control for simulated annealing: A comparative study. Computers & Operations Research 31:2439\u20132451","journal-title":"Computers & Operations Research"},{"issue":"4","key":"436_CR5","doi-asserted-by":"publisher","first-page":"1779","DOI":"10.1111\/coin.12466","volume":"37","author":"E Bagheri","year":"2021","unstructured":"Bagheri E, Dastghaibyfard G, Hamzeh A (2021) Faimcs: A fast and accurate influence maximization algorithm in social networks based on community structures. Comput Intell 37(4):1779\u20131802","journal-title":"Comput Intell"},{"key":"436_CR6","doi-asserted-by":"crossref","unstructured":"Bharathi S,\u00a0Kempe D,\u00a0Salek M. (2007) Competitive influence maximization in social networks. In: X.\u00a0Deng, F.\u00a0C. Graham (Eds.), Internet and Network Economics, Vol. 4858 of Lecture Notes in Computer Science, Springer, pp. 306\u2013311","DOI":"10.1007\/978-3-540-77105-0_31"},{"key":"436_CR7","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.ins.2020.12.048","volume":"556","author":"TK Biswas","year":"2021","unstructured":"Biswas TK, Abbasi A, Chakrabortty RK (2021) An mcdm integrated adaptive simulated annealing approach for influence maximization in social networks. Inf Sci 556:27\u201348","journal-title":"Inf Sci"},{"key":"436_CR8","doi-asserted-by":"crossref","unstructured":"Borodin A,\u00a0Filmus Y,\u00a0Oren J (2010) Threshold models for competitive influence in social networks, in: International workshop on internet and network economics, Springer, pp. 539\u2013550","DOI":"10.1007\/978-3-642-17572-5_48"},{"issue":"6","key":"436_CR9","doi-asserted-by":"publisher","first-page":"1188","DOI":"10.1016\/j.ipm.2016.05.006","volume":"52","author":"A Bozorgi","year":"2016","unstructured":"Bozorgi A, Haghighi H, Zahedi MS, Rezvani M (2016) Incim: A community-based algorithm for influence maximization problem under the linear threshold model. Information Processing & Management 52(6):1188\u20131199","journal-title":"Information Processing & Management"},{"key":"436_CR10","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.knosys.2017.07.029","volume":"134","author":"A Bozorgi","year":"2017","unstructured":"Bozorgi A, Samet S, Kwisthout J, Wareham T (2017) Community-based influence maximization in social networks under a competitive linear threshold model. Knowl-Based Syst 134:149\u2013158","journal-title":"Knowl-Based Syst"},{"key":"436_CR11","doi-asserted-by":"crossref","unstructured":"Bucur D,\u00a0Iacca G (2016) Influence maximization in social networks with genetic algorithms, in: Applications of Evolutionary Computation, pp. 379\u2013392","DOI":"10.1007\/978-3-319-31204-0_25"},{"key":"436_CR12","doi-asserted-by":"crossref","unstructured":"Cali\u00f2 A, Tagarelli A (2018) Trust-based dynamic linear threshold models for non-competitive and competitive influence propagation. In: 2018 17th IEEE international conference on trust, security and privacy in computing and communications\/12th IEEE international conference on big data science and engineering (TrustCom\/BigDataSE). IEEE:156\u2013162","DOI":"10.1109\/TrustCom\/BigDataSE.2018.00033"},{"key":"436_CR13","unstructured":"Cao Y, Li W, Wu Q, Chen E, Liu Q (2019) Competitive influence maximization in social networks: a stackelberg game perspective. IEEE Transactions on Computational Social Systems 6(3):396\u2013409"},{"issue":"3","key":"436_CR14","doi-asserted-by":"publisher","first-page":"435","DOI":"10.3390\/sym17030435","volume":"17","author":"B Chai","year":"2025","unstructured":"Chai B, Fu J, Zhang R, Tang J (2025) A landscape-aware discrete particle swarm optimization for the influence maximization problem in social networks. Symmetry 17(3):435\u2013435","journal-title":"Symmetry"},{"issue":"3","key":"436_CR15","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1007\/s10115-012-0540-7","volume":"33","author":"YC Chen","year":"2012","unstructured":"Chen YC, Peng WC, Lee SY (2012) Efficient algorithms for influence maximization in social networks. Knowledge & Information Systems 33(3):577\u2013601","journal-title":"Knowledge & Information Systems"},{"issue":"6","key":"436_CR16","first-page":"2815","volume":"33","author":"W Chen","year":"2021","unstructured":"Chen W, Wang Y, Yang S (2021) Efficient influence maximization in social networks with community structure. IEEE Trans Knowl Data Eng 33(6):2815\u20132828","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"436_CR17","doi-asserted-by":"publisher","first-page":"2210","DOI":"10.1109\/TCSS.2023.3272331","volume":"11","author":"T Chen","year":"2023","unstructured":"Chen T, Yan S, Guo J, Wu W (2023) Touplegdd: A fine-designed solution of influence maximization by deep reinforcement learning. IEEE Transactions on Computational Social Systems 11(2):2210\u20132221","journal-title":"IEEE Transactions on Computational Social Systems"},{"key":"436_CR18","doi-asserted-by":"crossref","unstructured":"Chen W,\u00a0Wang C,\u00a0Wang Y (2010) Scalable influence maximization for prevalent viral marketing in large-scale social networks, in: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, New York, NY, USA, pp. 1029\u20131038","DOI":"10.1145\/1835804.1835934"},{"key":"436_CR19","doi-asserted-by":"crossref","unstructured":"Chen W,\u00a0Wang Y,\u00a0Yang S (2009) Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 199\u2013208","DOI":"10.1145\/1557019.1557047"},{"key":"436_CR20","doi-asserted-by":"crossref","unstructured":"Gao S,\u00a0Xie W,\u00a0Shang J,\u00a0Liu D,\u00a0Qiang B (2021) Bacim: Balanced competitive influence maximization based on blocked reverse influence sampling. In: 2021 22nd IEEE International Conference on Mobile Data Management (MDM), IEEE, pp. 151\u2013156","DOI":"10.1109\/MDM52706.2021.00032"},{"issue":"12","key":"436_CR21","doi-asserted-by":"publisher","first-page":"7821","DOI":"10.1073\/pnas.122653799","volume":"99","author":"M Girvan","year":"2002","unstructured":"Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proceedings of the National Academy of Science 99(12):7821\u20137826","journal-title":"Proceedings of the National Academy of Science"},{"issue":"3","key":"436_CR22","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1109\/MCI.2016.2572538","volume":"11","author":"M Gong","year":"2016","unstructured":"Gong M, Song C, Duan C, Ma L, Shen B (2016) An efficient memetic algorithm for influence maximization in social networks. IEEE Comput Intell Mag 11(3):22\u201333","journal-title":"IEEE Comput Intell Mag"},{"key":"436_CR23","doi-asserted-by":"crossref","unstructured":"Goyal A,\u00a0Lu W, Lakshmanan LVS (2011), Celf++: optimizing the greedy algorithm for influence maximization in social networks. ACM","DOI":"10.1145\/1963192.1963217"},{"key":"436_CR24","first-page":"338","volume":"354","author":"Q He","year":"2019","unstructured":"He Q, Wang X, Lei Z, Huang M, Cai Y, Ma L (2019) Tifim: A two-stage iterative framework for influence maximization in social networks. Appl Math Comput 354:338\u2013352","journal-title":"Appl Math Comput"},{"key":"436_CR25","doi-asserted-by":"crossref","unstructured":"He X,\u00a0Song G,\u00a0Chen W,\u00a0Jiang Q (2012) Influence blocking maximization in social networks under the competitive linear threshold model. In: Proceedings of the 2012 siam international conference on data mining, SIAM, pp. 463\u2013474","DOI":"10.1137\/1.9781611972825.40"},{"key":"436_CR26","first-page":"91100","volume":"8","author":"X Hu","year":"2020","unstructured":"Hu X, Tan C, Zeng J (2020) An adaptive genetic algorithm for influence maximization in social networks. IEEE Access 8:91100\u201391112","journal-title":"IEEE Access"},{"key":"436_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106600","volume":"213","author":"H Huang","year":"2021","unstructured":"Huang H, Meng Z, Shen H (2021) Competitive and complementary influence maximization in social network: A follower\u2019s perspective. Knowl-Based Syst 213:106600","journal-title":"Knowl-Based Syst"},{"key":"436_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2021.126480","volume":"586","author":"J Jabari Lotf","year":"2022","unstructured":"Jabari Lotf J, Abdollahi Azgomi M, Ebrahimi Dishabi MR (2022) An improved influence maximization method for social networks based on genetic algorithm. XXPhys A 586:126480","journal-title":"XXPhys A"},{"key":"436_CR29","first-page":"696","volume":"33","author":"Q Jiang","year":"2019","unstructured":"Jiang Q, Song G, Cong G, Wang Y, Si W, Xie K (2019) Simulated annealing-based influence maximization in social networks. Proceedings of the AAAI Conference on Artificial Intelligence 33:696\u2013703","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"436_CR30","first-page":"137","volume":"4","author":"D Kempe","year":"2003","unstructured":"Kempe D, Kleinberg J, Tardos E (2003) Maximizing the spread of influence through a social network. Theory of Computing 4:137\u2013146","journal-title":"Theory of Computing"},{"key":"436_CR31","doi-asserted-by":"crossref","unstructured":"Kr\u00f6mer P,\u00a0Nowakov\u00e1 J (2017) Guided genetic algorithm for the influence maximization problem. In: International Computing and Combinatorics Conference, Springer, pp. 630\u2013641","DOI":"10.1007\/978-3-319-62389-4_52"},{"key":"436_CR32","doi-asserted-by":"crossref","unstructured":"Kumar S,\u00a0Zhang X,\u00a0Leskovec J (2019) Predicting dynamic embedding trajectory in temporal interaction networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, ACM, pp. 1269\u20131278","DOI":"10.1145\/3292500.3330895"},{"issue":"2","key":"436_CR33","first-page":"1","volume":"5","author":"S-Y Lee","year":"2014","unstructured":"Lee S-Y, Peng W-C, Chen Y-C, Zhu W-Y, Lee W-C (2014) Cim: Community-based influence maximization in social networks. ACM Transactions on Intelligent Systems and Technology 5(2):1\u201331","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"issue":"1","key":"436_CR34","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1145\/1232722.1232727","volume":"1","author":"J Leskovec","year":"2007","unstructured":"Leskovec J, Adamic LA, Huberman BA (2007) The dynamics of viral marketing. ACM Trans Web 1(1):5","journal-title":"ACM Trans Web"},{"key":"436_CR35","first-page":"105","volume":"128","author":"Y Li","year":"2021","unstructured":"Li Y, Zhang Y, Wu J (2021) Adaptive genetic algorithm for influence maximization with dynamic parameter adjustment. Computers & Operations Research 128:105\u2013118","journal-title":"Computers & Operations Research"},{"issue":"3","key":"436_CR36","doi-asserted-by":"publisher","first-page":"1288","DOI":"10.1109\/TCSS.2022.3164667","volume":"10","author":"H Li","year":"2022","unstructured":"Li H, Xu M, Bhowmick SS, Rayhan JS, Sun C, Cui J (2022) Piano: Influence maximization meets deep reinforcement learning. IEEE Transactions on Computational Social Systems 10(3):1288\u20131300","journal-title":"IEEE Transactions on Computational Social Systems"},{"key":"436_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110062","volume":"136","author":"W Li","year":"2023","unstructured":"Li W, Hu Y, Jiang C, Wu S, Bai QW, Lai E (2023) Abem: An adaptive agent-based evolutionary approach for influence maximization in dynamic social networks. Appl Soft Comput 136:110062","journal-title":"Appl Soft Comput"},{"key":"436_CR38","unstructured":"Ling C,\u00a0Jiang J,\u00a0Wang J, Thai MT,\u00a0Xue R,\u00a0Song J,\u00a0Qiu M,\u00a0Zhao L (2023) Deep graph representation learning and optimization for influence maximization. In: International conference on machine learning, PMLR, pp. 21350\u201321361"},{"issue":"2","key":"436_CR39","first-page":"328","volume":"24","author":"H Liu","year":"2020","unstructured":"Liu H, Wang S, Zhao J (2020) Improving genetic algorithms for influence maximization: A hybrid approach with simulated annealing. IEEE Trans Evol Comput 24(2):328\u2013342","journal-title":"IEEE Trans Evol Comput"},{"key":"436_CR40","unstructured":"Lu W,\u00a0Xiao X,\u00a0Goyal A,\u00a0Huang K, Lakshmanan LVS (2017) Refutations on \u201cdebunking the myths of influence maximization: An in-depth benchmarking study\u201d"},{"key":"436_CR41","doi-asserted-by":"publisher","first-page":"19307","DOI":"10.1038\/srep19307","volume":"6","author":"FD Malliaros","year":"2016","unstructured":"Malliaros FD, Rossi MEG, Vazirgiannis M (2016) Locating influential nodes in complex networks. Sci Rep 6:19307","journal-title":"Sci Rep"},{"key":"436_CR42","doi-asserted-by":"crossref","unstructured":"Ma H,\u00a0Yang H, Lyu MR,\u00a0King I (2008) Mining social networks using heat diffusion processes for marketing candidates selection. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, ACM, pp. 233\u2013242","DOI":"10.1145\/1458082.1458115"},{"key":"436_CR43","doi-asserted-by":"publisher","DOI":"10.1093\/oso\/9780198805090.001.0001","volume-title":"Networks","author":"M Newman","year":"2018","unstructured":"Newman M (2018) Networks. Oxford University Press, Oxford"},{"key":"436_CR44","doi-asserted-by":"crossref","unstructured":"Nguyen HT, Dinh TN, Thaip MT (2016) Cost-aware targeted viral marketing in billion-scale networks. IEEE","DOI":"10.1109\/INFOCOM.2016.7524377"},{"issue":"9","key":"436_CR45","doi-asserted-by":"publisher","first-page":"4398","DOI":"10.1109\/TKDE.2020.3040028","volume":"34","author":"G Panagopoulos","year":"2020","unstructured":"Panagopoulos G, Malliaros FD, Vazirgiannis M (2020) Multi-task learning for influence estimation and maximization. IEEE Trans Knowl Data Eng 34(9):4398\u20134409","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"10","key":"436_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2021.126258","volume":"582","author":"AM Samir","year":"2021","unstructured":"Samir AM, Rady S, Gharib TF (2021) Lkg: A fast scalable community-based approach for influence maximization problem in social networks. XXPhys A 582(10):126258","journal-title":"XXPhys A"},{"key":"436_CR47","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1016\/j.physa.2018.09.142","volume":"514","author":"B Singh","year":"2019","unstructured":"Singh B, Biswas K (2019) C2im: Community based context-aware influence maximization in social networks, Physica, A. Statistical mechanics and its applications 514:796\u2013818","journal-title":"Statistical mechanics and its applications"},{"key":"436_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105554","volume":"82","author":"SS Singh","year":"2019","unstructured":"Singh SS, Kumar A, Singh K, Biswas B (2019) Lapso-im: A learning-based influence maximization approach for social networks. Appl Soft Comput 82:105554","journal-title":"Appl Soft Comput"},{"issue":"06","key":"436_CR49","doi-asserted-by":"publisher","first-page":"2450232","DOI":"10.1142\/S0129183124502322","volume":"36","author":"J Tang","year":"2025","unstructured":"Tang J, Du Q (2025) An adaptive differential evolution algorithm driven by multiple probabilistic mutation strategies for influence maximization in social networks. Int J Mod Phys C 36(06):2450232","journal-title":"Int J Mod Phys C"},{"key":"436_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.07.004","volume":"187","author":"J Tang","year":"2020","unstructured":"Tang J, Zhang R, Wang P, Zhao Z, Fan L, Liu X (2020) A discrete shuffled frog-leaping algorithm to identify influential nodes for influence maximization in social networks. Knowl-Based Syst 187:104833","journal-title":"Knowl-Based Syst"},{"key":"436_CR51","doi-asserted-by":"crossref","unstructured":"Tsai C,\u00a0Yang Y,\u00a0Chiang M (2015) A genetic newgreedy algorithm for influence maximization in social network, in: 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 2549\u20132554","DOI":"10.1109\/SMC.2015.446"},{"issue":"7","key":"436_CR52","doi-asserted-by":"publisher","first-page":"1124","DOI":"10.14778\/3450980.3450981","volume":"14","author":"D Tsaras","year":"2021","unstructured":"Tsaras D, Trimponias G, Ntaflos L, Papadias D (2021) Collective influence maximization for multiple competing products with an awareness-to-influence model. Proceedings of the VLDB Endowment 14(7):1124\u20131136","journal-title":"Proceedings of the VLDB Endowment"},{"issue":"3","key":"436_CR53","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1007\/s10618-012-0262-1","volume":"25","author":"C Wang","year":"2012","unstructured":"Wang C, Chen W, Wang Y (2012) Scalable influence maximization for independent cascade model in large-scale social networks. Data Mining & Knowledge Discovery 25(3):545\u2013576","journal-title":"Data Mining & Knowledge Discovery"},{"key":"436_CR54","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.comnet.2017.05.004","volume":"123","author":"P Wu","year":"2017","unstructured":"Wu P, Pan L (2017) Scalable influence blocking maximization in social networks under competitive independent cascade models. Comput Netw 123:38\u201350","journal-title":"Comput Netw"},{"key":"436_CR55","unstructured":"Wu D, Chen X, Huang G, Wang C (2022) Diversity-preserving genetic algorithm for influence maximization in social networks. IEEE Trans Knowl Data Eng 34(2):832\u2013845"},{"key":"436_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105139","volume":"114","author":"Z Xiangbing","year":"2022","unstructured":"Xiangbing Z, Hongjiang M, Jianggang G, Huiling C, Wu D (2022) Parameter adaptation-based ant colony optimization with dynamic hybrid mechanism. Eng Appl Artif Intell 114:105139","journal-title":"Eng Appl Artif Intell"},{"key":"436_CR57","doi-asserted-by":"crossref","unstructured":"Xu Z, Zhang X, Chen M, Xu L (2024) Influence maximization in mobile social networks based on rwp-celf. Computing 106(6):1913\u20131931","DOI":"10.1007\/s00607-024-01276-z"},{"issue":"10","key":"436_CR58","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-016-9080-3","volume":"60","author":"D Yang","year":"2017","unstructured":"Yang D, Liao X, Shen H, Cheng X, Chen G (2017) Relative influence maximization in competitive social networks. SCIENCE CHINA Inf Sci 60(10):108101","journal-title":"SCIENCE CHINA Inf Sci"},{"key":"436_CR59","first-page":"209","volume":"26","author":"X Yao","year":"2018","unstructured":"Yao X, Liu Y, Lin X (2018) Hybrid genetic algorithm and simulated annealing for influence maximization in social networks. Journal of Computational Science 26:209\u2013220","journal-title":"Journal of Computational Science"},{"key":"436_CR60","doi-asserted-by":"crossref","unstructured":"Yu Y,\u00a0Jia J,\u00a0Li D,\u00a0Zhu Y (2017) Fair multi-influence maximization in competitive social networks. In: International Conference on Wireless Algorithms, Systems, and Applications, Springer, pp. 253\u2013265","DOI":"10.1007\/978-3-319-60033-8_23"},{"key":"436_CR61","doi-asserted-by":"crossref","unstructured":"Zhang C-X (2016) Yi-Cheng, Lu, Xin, Zi-Ke, Zhan, Xiu-Xiu, Liu, Chuang, Dynamics of information diffusion and its applications on complex networks, Physics Reports A. Review 651:1\u201334","DOI":"10.1016\/j.physrep.2016.07.002"},{"key":"436_CR62","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.physa.2017.02.067","volume":"478","author":"K Zhang","year":"2017","unstructured":"Zhang K, Du H, Feldman MW (2017) Maximizing influence in a social network: Improved results using a genetic algorithm. XXPhys A 478:20\u201330","journal-title":"XXPhys A"},{"key":"436_CR63","doi-asserted-by":"publisher","first-page":"100664","DOI":"10.1016\/j.swevo.2020.100664","volume":"54","author":"G Zhang","year":"2020","unstructured":"Zhang G, Hu Y, Sun J, Zhang W (2020) An improved genetic algorithm for the flexible job shop scheduling problem with multiple time constraints. Swarm Evol Comput 54:100664\u2013100664","journal-title":"Swarm Evol Comput"},{"key":"436_CR64","unstructured":"Zuo J,\u00a0Liu X,\u00a0Joe-Wong C, Lui JC,\u00a0Chen W (2022) Online competitive influence maximization, in: International conference on artificial intelligence and statistics, PMLR, pp. 11472\u201311502"}],"container-title":["Journal of King Saud University Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44443-025-00436-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00436-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00436-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T15:49:50Z","timestamp":1773071390000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44443-025-00436-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,24]]},"references-count":64,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["436"],"URL":"https:\/\/doi.org\/10.1007\/s44443-025-00436-1","relation":{},"ISSN":["1319-1578","2213-1248"],"issn-type":[{"value":"1319-1578","type":"print"},{"value":"2213-1248","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,24]]},"assertion":[{"value":"26 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that no conflicts of interest are associated with this research.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"88"}}