{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:05:11Z","timestamp":1775815511009,"version":"3.50.1"},"reference-count":55,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T00:00:00Z","timestamp":1672358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872093"],"award-info":[{"award-number":["61872093"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62206303"],"award-info":[{"award-number":["62206303"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U20B2051"],"award-info":[{"award-number":["U20B2051"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,2,17]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Maximizing influences in complex networks is a practically important but computationally challenging task for social network analysis, due to its nondeterministic polynomial time (NP)-hard nature. Most current approximation or heuristic methods either require tremendous human design efforts or achieve unsatisfying balances between effectiveness and efficiency. Recent machine learning attempts only focus on speed but lack performance enhancement. In this paper, different from previous attempts, we propose an effective deep reinforcement learning model that achieves superior performances over traditional best influence maximization algorithms. Specifically, we design an end-to-end learning framework that combines graph neural network as the encoder and reinforcement learning as the decoder, named DREIM. Through extensive training on small synthetic graphs, DREIM outperforms the state-of-the-art baseline methods on very large synthetic and real-world networks on solution quality, and we also empirically show its linear scalability with regard to the network size, which demonstrates its superiority in solving this problem.<\/jats:p>","DOI":"10.1093\/comjnl\/bxac187","type":"journal-article","created":{"date-parts":[[2022,12,31]],"date-time":"2022-12-31T06:23:03Z","timestamp":1672467783000},"page":"463-473","source":"Crossref","is-referenced-by-count":12,"title":["Finding Influencers in Complex Networks: An Effective Deep Reinforcement Learning Approach"],"prefix":"10.1093","volume":"67","author":[{"given":"Changan","family":"Liu","sequence":"first","affiliation":[{"name":"Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University , Shanghai 200433 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changjun","family":"Fan","sequence":"additional","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology , Changsha , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongzhi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University , Shanghai 200433 , China"},{"name":"Institute of Intelligent Complex Systems, Fudan University , Shanghai 200433 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,12,30]]},"reference":[{"key":"2024021913301448800_ref1","first-page":"695","volume-title":"Proceedings of the International Conference on Management of Data,","author":"Nguyen","year":"2016"},{"key":"2024021913301448800_ref2","first-page":"1029","volume-title":"Proceedings of the 16th International Conference on Knowledge Discovery and Data Mining,","author":"Chen","year":"2010"},{"key":"2024021913301448800_ref3","first-page":"137","volume-title":"Proceedings of the 9th International Conference on Knowledge Discovery and Data Mining,","author":"Kempe","year":"2003"},{"key":"2024021913301448800_ref4","first-page":"991","volume-title":"Proceedings of the International Conference on Management of Data,","author":"Tang","year":"2018"},{"key":"2024021913301448800_ref5","first-page":"88","volume-title":"Proceedings of the International Conference on Data Mining,","author":"Chen","year":"2010"},{"key":"2024021913301448800_ref6","first-page":"509","volume-title":"Proceedings of the 22nd International Conference on Information and Knowledge Management,","author":"Cheng","year":"2013"},{"key":"2024021913301448800_ref7","first-page":"475","volume-title":"Proceedings of the 37th International Conference on Research & Development in Information Retrieval,","author":"Cheng","year":"2014"},{"key":"2024021913301448800_ref8","first-page":"629","volume-title":"Proceedings of the 23rd International Conference on Information and Knowledge Management,","author":"Cohen","year":"2014"},{"key":"2024021913301448800_ref9","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1145\/2740908.2742725","volume-title":"Proceedings of the 24th International Conference on World Wide Web,","author":"Galhotra","year":"2015"},{"key":"2024021913301448800_ref10","first-page":"743","volume-title":"Proceedings of the International Conference on Management of Data,","author":"Galhotra","year":"2016"},{"key":"2024021913301448800_ref11","first-page":"211","volume-title":"Proceedings of the International Conference on Data Mining,","author":"Goyal","year":"2011"},{"key":"2024021913301448800_ref12","doi-asserted-by":"crossref","first-page":"73","DOI":"10.14778\/2047485.2047492","article-title":"A data-based approach to social influence maximization","volume":"5","author":"Goyal","year":"2011","journal-title":"Proc. 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