{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:49:11Z","timestamp":1773798551698,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":48,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T00:00:00Z","timestamp":1689120000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100011033","name":"Agencia Estatal de Investigaci&oacute;n","doi-asserted-by":"publisher","award":["TED2021-129319B-I00"],"award-info":[{"award-number":["TED2021-129319B-I00"]}],"id":[{"id":"10.13039\/501100011033","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011033","name":"Agencia Estatal de Investigaci&oacute;n","doi-asserted-by":"publisher","award":["PID2019-104156GB-I00"],"award-info":[{"award-number":["PID2019-104156GB-I00"]}],"id":[{"id":"10.13039\/501100011033","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,7,15]]},"DOI":"10.1145\/3583131.3590396","type":"proceedings-article","created":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T19:40:19Z","timestamp":1689190819000},"page":"357-366","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Q-Learning Ant Colony Optimization supported by Deep Learning for Target Set Selection"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7116-1616","authenticated-orcid":false,"given":"Jairo Enrique","family":"Ram\u00edrez S\u00e1nchez","sequence":"first","affiliation":[{"name":"Tecnologico de Monterrey, Monterrey, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8543-9893","authenticated-orcid":false,"given":"Camilo","family":"Chac\u00f3n Sartori","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Institute (IIIA-CSIC), Bellaterra, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1736-3559","authenticated-orcid":false,"given":"Christian","family":"Blum","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Institute (IIIA-CSIC), Bellaterra, Spain"}]}],"member":"320","published-online":{"date-parts":[[2023,7,12]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tcs.2010.08.021"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.disopt.2010.09.007"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2020.07.063"},{"key":"e_1_3_2_1_4_1","unstructured":"Chen Cai and Yusu Wang. 2020. A Note on Over-Smoothing for Graph Neural Networks. (2020). arXiv:2006.13318 http:\/\/arxiv.org\/abs\/2006.13318  Chen Cai and Yusu Wang. 2020. A Note on Over-Smoothing for Graph Neural Networks. (2020). arXiv:2006.13318 http:\/\/arxiv.org\/abs\/2006.13318"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.4230\/LIPIcs.APPROX-RANDOM.2016.4"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1137\/08073617X"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835934"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557047"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00224-013-9499-3"},{"key":"e_1_3_2_1_11_1","volume-title":"Combinatorial Optimization and Applications","author":"Cordasco Gennaro","unstructured":"Gennaro Cordasco , Luisa Gargano , Marco Mecchia , Adele A Rescigno , and Ugo Vaccaro . 2015. A fast and effective heuristic for discovering small target sets in social networks . In Combinatorial Optimization and Applications . Springer , 193--208. Gennaro Cordasco, Luisa Gargano, Marco Mecchia, Adele A Rescigno, and Ugo Vaccaro. 2015. A fast and effective heuristic for discovering small target sets in social networks. In Combinatorial Optimization and Applications. Springer, 193--208."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2808797.2808888"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-016-0408-z"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2598394.2602287"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-011-9208-z"},{"key":"e_1_3_2_1_16_1","volume-title":"Ant Colony Optimization","author":"Dorigo Marco","unstructured":"Marco Dorigo and Thomas St\u00fctzle . 2004. Ant Colony Optimization . The MIT Press . Marco Dorigo and Thomas St\u00fctzle. 2004. Ant Colony Optimization. The MIT Press."},{"key":"e_1_3_2_1_17_1","volume-title":"Adding Negative Learning to Ant Colony Optimization: A Comprehensive Study. Mathematics 9, 4","author":"Dummy Author","year":"2021","unstructured":"Author Dummy . 2021. Adding Negative Learning to Ant Colony Optimization: A Comprehensive Study. Mathematics 9, 4 ( 2021 ). Author Dummy. 2021. Adding Negative Learning to Ant Colony Optimization: A Comprehensive Study. Mathematics 9, 4 (2021)."},{"key":"e_1_3_2_1_18_1","volume-title":"Proceedings of the Genetic and Evolutionary Computation Conference","author":"Dummy Author","year":"2022","unstructured":"Author Dummy . 2022 . A Biased Random Key Genetic Algorithm Applied to Target Set Selection in Viral Marketing . In Proceedings of the Genetic and Evolutionary Computation Conference ( Boston, Massachusetts) (GECCO '22). Association for Computing Machinery, New York, NY, USA, 241--250. Author Dummy. 2022. A Biased Random Key Genetic Algorithm Applied to Target Set Selection in Viral Marketing. In Proceedings of the Genetic and Evolutionary Computation Conference (Boston, Massachusetts) (GECCO '22). Association for Computing Machinery, New York, NY, USA, 241--250."},{"key":"e_1_3_2_1_19_1","volume-title":"Proceedigns of FedCSIS 2022 - 17th Conference on Computer Science and Intelligence Systems","author":"Dummy Author","unstructured":"Author Dummy . 2022. Boosting a Genetic Algorithm with Graph Neural Networks for Multi-Hop Influence Maximization in Social Networks . In Proceedigns of FedCSIS 2022 - 17th Conference on Computer Science and Intelligence Systems . IEEE , 363--371. Author Dummy. 2022. Boosting a Genetic Algorithm with Graph Neural Networks for Multi-Hop Influence Maximization in Social Networks. In Proceedigns of FedCSIS 2022 - 17th Conference on Computer Science and Intelligence Systems. IEEE, 363--371."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.disopt.2019.05.004"},{"key":"e_1_3_2_1_21_1","volume-title":"Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds.","author":"Fey Matthias","unstructured":"Matthias Fey and Jan E. Lenssen . 2019 . Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds. Matthias Fey and Jan E. Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1570-8667(03)00022-4"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1037\/a0032097"},{"key":"e_1_3_2_1_24_1","first-page":"1","article-title":"Using complex systems analysis to advance marketing theory development: Modeling heterogeneity effects on new product growth through stochastic cellular automata","volume":"9","author":"Goldenberg Jacob","year":"2001","unstructured":"Jacob Goldenberg , Barak Libai , and Eitan Muller . 2001 . Using complex systems analysis to advance marketing theory development: Modeling heterogeneity effects on new product growth through stochastic cellular automata . Academy of Marketing Science Review 9 , 3 (2001), 1 -- 18 . Jacob Goldenberg, Barak Libai, and Eitan Muller. 2001. Using complex systems analysis to advance marketing theory development: Modeling heterogeneity effects on new product growth through stochastic cellular automata. Academy of Marketing Science Review 9, 3 (2001), 1--18.","journal-title":"Academy of Marketing Science Review"},{"key":"e_1_3_2_1_25_1","volume-title":"Threshold models of collective behavior. American journal of sociology 83, 6","author":"Granovetter Mark","year":"1978","unstructured":"Mark Granovetter . 1978. Threshold models of collective behavior. American journal of sociology 83, 6 ( 1978 ), 1420--1443. Mark Granovetter. 1978. Threshold models of collective behavior. American journal of sociology 83, 6 (1978), 1420--1443."},{"key":"e_1_3_2_1_26_1","volume-title":"Proceedings of the 7th Python in Science Conference, Ga\u00ebl Varoquaux, Travis Vaught, and Jarrod Millman (Eds.). Pasadena, CA USA, 11 -- 15","author":"Hagberg Aric A.","unstructured":"Aric A. Hagberg , Daniel A. Schult , and Pieter J. Swart . 2008. Exploring Network Structure, Dynamics, and Function using NetworkX . In Proceedings of the 7th Python in Science Conference, Ga\u00ebl Varoquaux, Travis Vaught, and Jarrod Millman (Eds.). Pasadena, CA USA, 11 -- 15 . Aric A. Hagberg, Daniel A. Schult, and Pieter J. Swart. 2008. Exploring Network Structure, Dynamics, and Function using NetworkX. In Proceedings of the 7th Python in Science Conference, Ga\u00ebl Varoquaux, Travis Vaught, and Jarrod Millman (Eds.). Pasadena, CA USA, 11 -- 15."},{"key":"e_1_3_2_1_27_1","volume-title":"Garnett (Eds.)","volume":"30","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton , Zhitao Ying , and Jure Leskovec . 2017 . Inductive Representation Learning on Large Graphs. In Advances in Neural Information Processing Systems, I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R . Garnett (Eds.) , Vol. 30 . Curran Associates, Inc. Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In Advances in Neural Information Processing Systems, I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-73117-9_10"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1011122126881"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/956750.956769"},{"key":"e_1_3_2_1_31_1","volume-title":"Garnett (Eds.)","volume":"30","author":"Khalil Elias","year":"2017","unstructured":"Elias Khalil , Hanjun Dai , Yuyu Zhang , Bistra Dilkina , and Le Song . 2017 . Learning Combinatorial Optimization Algorithms over Graphs. In Advances in Neural Information Processing Systems, I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R . Garnett (Eds.) , Vol. 30 . Curran Associates, Inc. https:\/\/proceedings.neurips.cc\/paper\/ 2017\/file\/d9896106ca98d3d05b8cbdf4fd8b13a1-Paper.pdf Elias Khalil, Hanjun Dai, Yuyu Zhang, Bistra Dilkina, and Le Song. 2017. Learning Combinatorial Optimization Algorithms over Graphs. In Advances in Neural Information Processing Systems, I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc. https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/d9896106ca98d3d05b8cbdf4fd8b13a1-Paper.pdf"},{"key":"e_1_3_2_1_32_1","unstructured":"Jure Leskovec and Andrej Krevl. 2014. SNAP Datasets: Stanford Large Network Dataset Collection. http:\/\/snap.stanford.edu\/data.  Jure Leskovec and Andrej Krevl. 2014. SNAP Datasets: Stanford Large Network Dataset Collection. http:\/\/snap.stanford.edu\/data."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2011.99"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.orp.2016.09.002"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1177\/002224299005400101"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-012-0067-7"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106623"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CEC48606.2020.9185558"},{"key":"e_1_3_2_1_39_1","volume-title":"International Workshop on Algorithms and Computation. Springer, 240--251","author":"Thirumala Reddy TV","year":"2010","unstructured":"TV Thirumala Reddy , D Sai Krishna , and C Pandu Rangan . 2010 . Variants of spreading messages . In International Workshop on Algorithms and Computation. Springer, 240--251 . TV Thirumala Reddy, D Sai Krishna, and C Pandu Rangan. 2010. Variants of spreading messages. In International Workshop on Algorithms and Computation. Springer, 240--251."},{"key":"e_1_3_2_1_40_1","volume-title":"Markus Hagenbuchner, and Gabriele Monfardini.","author":"Scarselli Franco","year":"2008","unstructured":"Franco Scarselli , Marco Gori , Ah Chung Tsoi , Markus Hagenbuchner, and Gabriele Monfardini. 2008 . The graph neural network model. IEEE transactions on neural networks 20, 1 (2008), 61--80. Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2008. The graph neural network model. IEEE transactions on neural networks 20, 1 (2008), 61--80."},{"key":"e_1_3_2_1_41_1","volume-title":"Micromotives and macrobehavior","author":"Schelling Thomas C","unstructured":"Thomas C Schelling . 2006. Micromotives and macrobehavior . WW Norton & Company . Thomas C Schelling. 2006. Micromotives and macrobehavior. WW Norton & Company."},{"key":"e_1_3_2_1_42_1","volume-title":"Diffusion in social networks","author":"Shakarian Paulo","unstructured":"Paulo Shakarian , Abhivav Bhatnagar , Ashkan Aleali , Elham Shaabani , and Ruocheng Guo . 2015. Diffusion in social networks . Springer . Paulo Shakarian, Abhivav Bhatnagar, Ashkan Aleali, Elham Shaabani, and Ruocheng Guo. 2015. Diffusion in social networks. Springer."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-013-0135-7"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2022.105769"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2013.09.033"},{"key":"e_1_3_2_1_46_1","volume-title":"Machine learning 8","author":"Watkins Christopher JCH","year":"1992","unstructured":"Christopher JCH Watkins and Peter Dayan . 1992. Q-learning. Machine learning 8 ( 1992 ), 279--292. Christopher JCH Watkins and Peter Dayan. 1992. Q-learning. Machine learning 8 (1992), 279--292."},{"key":"e_1_3_2_1_47_1","volume-title":"Lin (Eds.)","volume":"33","author":"Zhang Cong","year":"2020","unstructured":"Cong Zhang , Wen Song , Zhiguang Cao , Jie Zhang , Puay Siew Tan , and Xu Chi . 2020 . Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H . Lin (Eds.) , Vol. 33 . Curran Associates, Inc., 1621--1632. https:\/\/proceedings.neurips.cc\/paper\/ 2020\/file\/11958dfee29b6709f48a9ba0387a2431-Paper.pdf Cong Zhang, Wen Song, Zhiguang Cao, Jie Zhang, Puay Siew Tan, and Xu Chi. 2020. Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 1621--1632. https:\/\/proceedings.neurips.cc\/paper\/2020\/file\/11958dfee29b6709f48a9ba0387a2431-Paper.pdf"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"}],"event":{"name":"GECCO '23: Genetic and Evolutionary Computation Conference","location":"Lisbon Portugal","acronym":"GECCO '23","sponsor":["SIGEVO ACM Special Interest Group on Genetic and Evolutionary Computation"]},"container-title":["Proceedings of the Genetic and Evolutionary Computation Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583131.3590396","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3583131.3590396","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:48:49Z","timestamp":1750182529000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583131.3590396"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,12]]},"references-count":48,"alternative-id":["10.1145\/3583131.3590396","10.1145\/3583131"],"URL":"https:\/\/doi.org\/10.1145\/3583131.3590396","relation":{},"subject":[],"published":{"date-parts":[[2023,7,12]]},"assertion":[{"value":"2023-07-12","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}