{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T18:14:58Z","timestamp":1760552098480,"version":"3.37.3"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T00:00:00Z","timestamp":1712016000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T00:00:00Z","timestamp":1712016000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61772102","61772102","61772102","61772102"],"award-info":[{"award-number":["61772102","61772102","61772102","61772102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001602","name":"Science Foundation Ireland","doi-asserted-by":"publisher","award":["SFI\/12\/RC\/2289_P2"],"award-info":[{"award-number":["SFI\/12\/RC\/2289_P2"]}],"id":[{"id":"10.13039\/501100001602","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Computing"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s00607-024-01279-w","type":"journal-article","created":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T04:44:50Z","timestamp":1712033090000},"page":"1963-1986","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Unraveling human social behavior motivations via inverse reinforcement learning-based link prediction"],"prefix":"10.1007","volume":"106","author":[{"given":"Xin","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongbo","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liping","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomas E.","family":"Ward","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"V\u00e1clav","family":"Sn\u00e1\u0161el","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,2]]},"reference":[{"issue":"6","key":"1279_CR1","doi-asserted-by":"publisher","first-page":"1461","DOI":"10.1007\/s00607-022-01061-w","volume":"104","author":"W Du","year":"2022","unstructured":"Du W, Li G, He X (2022) Network structure optimization for social networks by minimizing the average path length. Computing 104(6):1461\u20131480. https:\/\/doi.org\/10.1007\/s00607-022-01061-w","journal-title":"Computing"},{"issue":"5","key":"1279_CR2","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.1007\/s00607-021-01035-4","volume":"104","author":"A Kumari","year":"2022","unstructured":"Kumari A, Behera RK, Sahoo B et al (2022) Prediction of link evolution using community detection in social network. Computing 104(5):1077\u20131098. https:\/\/doi.org\/10.1007\/s00607-021-01035-4","journal-title":"Computing"},{"issue":"6","key":"1279_CR3","doi-asserted-by":"publisher","first-page":"1141","DOI":"10.1007\/s00607-022-01113-1","volume":"105","author":"D Flores-Martin","year":"2023","unstructured":"Flores-Martin D, Berrocal J, Garc\u00eda-Alonso J et al (2023) Towards dynamic and heterogeneous social IoT environments. Computing 105(6):1141\u20131164. https:\/\/doi.org\/10.1007\/s00607-022-01113-1","journal-title":"Computing"},{"key":"1279_CR4","doi-asserted-by":"publisher","unstructured":"Chen J, Xu X, Wu Y, et\u00a0al (2018) GC-LSTM: graph convolution embedded LSTM for dynamic link prediction. https:\/\/doi.org\/10.48550\/arXiv.1812.04206, arXiv:1812.04206","DOI":"10.48550\/arXiv.1812.04206"},{"issue":"6","key":"1279_CR5","doi-asserted-by":"publisher","first-page":"3699","DOI":"10.1109\/TSMC.2019.2932913","volume":"51","author":"J Chen","year":"2021","unstructured":"Chen J, Zhang J, Xu X et al (2021) E-LSTM-D: a deep learning framework for dynamic network link prediction. IEEE Trans Syst Man Cybern Syst 51(6):3699\u20133712","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"issue":"1","key":"1279_CR6","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1109\/TBDATA.2020.3032755","volume":"7","author":"V La Gatta","year":"2021","unstructured":"La Gatta V, Moscato V, Postiglione M et al (2021) An epidemiological neural network exploiting dynamic graph structured data applied to the covid-19 outbreak. IEEE Trans Big Data 7(1):45\u201355","journal-title":"IEEE Trans Big Data"},{"issue":"12","key":"1279_CR7","doi-asserted-by":"publisher","first-page":"4946","DOI":"10.1109\/TCYB.2019.2920268","volume":"50","author":"M Yang","year":"2020","unstructured":"Yang M, Liu J, Chen L et al (2020) An advanced deep generative framework for temporal link prediction in dynamic networks. IEEE Trans Cybern 50(12):4946\u20134957","journal-title":"IEEE Trans Cybern"},{"issue":"6","key":"1279_CR8","doi-asserted-by":"publisher","first-page":"2493","DOI":"10.1109\/TKDE.2019.2957786","volume":"33","author":"F Feng","year":"2021","unstructured":"Feng F, He X, Tang J et al (2021) Graph adversarial training: dynamically regularizing based on graph structure. IEEE Trans Knowl Data Eng 33(6):2493\u20132504","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"12","key":"1279_CR9","doi-asserted-by":"publisher","first-page":"3042","DOI":"10.1109\/TSP.2014.2321121","volume":"62","author":"A Sandryhaila","year":"2014","unstructured":"Sandryhaila A, Moura JMF (2014) Discrete signal processing on graphs: frequency analysis. IEEE Trans Signal Process 62(12):3042\u20133054","journal-title":"IEEE Trans Signal Process"},{"key":"1279_CR10","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/j.neucom.2022.07.030","volume":"505","author":"L Yang","year":"2022","unstructured":"Yang L, Jiang X, Ji Y et al (2022) Gated graph convolutional network based on spatio-temporal semi-variogram for link prediction in dynamic complex network. Neurocomputing 505:289\u2013303","journal-title":"Neurocomputing"},{"issue":"1","key":"1279_CR11","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2021","unstructured":"Wu Z, Pan S, Chen F et al (2021) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32(1):4\u201324","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"5","key":"1279_CR12","doi-asserted-by":"publisher","first-page":"1979","DOI":"10.1109\/TCYB.2018.2818941","volume":"49","author":"Q Li","year":"2019","unstructured":"Li Q, Shen B, Wang Z et al (2019) Synchronization control for a class of discrete time-delay complex dynamical networks: a dynamic event-triggered approach. IEEE Trans Cybern 49(5):1979\u20131986","journal-title":"IEEE Trans Cybern"},{"key":"1279_CR13","doi-asserted-by":"crossref","unstructured":"Li K, Rath M, Burdick JW (2018) Inverse reinforcement learning via function approximation for clinical motion analysis. In: 2018 IEEE international conference on robotics and automation (ICRA), pp 610\u2013617","DOI":"10.1109\/ICRA.2018.8460563"},{"key":"1279_CR14","doi-asserted-by":"crossref","unstructured":"Naumann M, Sun L, Zhan W, et\u00a0al (2020) Analyzing the suitability of cost functions for explaining and imitating human driving behavior based on inverse reinforcement learning. In: 2020 IEEE international conference on robotics and automation (ICRA), pp 5481\u20135487","DOI":"10.1109\/ICRA40945.2020.9196795"},{"issue":"4","key":"1279_CR15","doi-asserted-by":"publisher","first-page":"5355","DOI":"10.1109\/LRA.2020.3005126","volume":"5","author":"Z Wu","year":"2020","unstructured":"Wu Z, Sun L, Zhan W et al (2020) Efficient sampling-based maximum entropy inverse reinforcement learning with application to autonomous driving. IEEE Robot Autom Lett 5(4):5355\u20135362","journal-title":"IEEE Robot Autom Lett"},{"key":"1279_CR16","unstructured":"Hamilton WL, Ying R, Leskovec J (2017) Inductive representation learning on large graphs. In: Proceedings of the 31st international conference on neural information processing systems. Curran Associates Inc., Red Hook, NY, USA, NIPS\u201917, pp 1025\u20131035"},{"key":"1279_CR17","doi-asserted-by":"publisher","DOI":"10.1007\/s00607-023-01178-6","author":"Z Zhao","year":"2023","unstructured":"Zhao Z, Lin S (2023) A cross-linguistic entity alignment method based on graph convolutional neural network and graph attention network. Computing. https:\/\/doi.org\/10.1007\/s00607-023-01178-6","journal-title":"Computing"},{"key":"1279_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108215","volume":"121","author":"J Wang","year":"2022","unstructured":"Wang J, Liang J, Yao K et al (2022) Graph convolutional autoencoders with co-learning of graph structure and node attributes. Pattern Recogn 121:108215. https:\/\/doi.org\/10.1016\/j.patcog.2021.108215","journal-title":"Pattern Recogn"},{"key":"1279_CR19","doi-asserted-by":"crossref","unstructured":"Pareja A, Domeniconi G, Chen J, et\u00a0al (2020) EvolveGCN: evolving graph convolutional networks for dynamic graphs. In: Proceedings of the 34th AAAI conference on artificial intelligence. AAAI Press, Palo Alto, CA, pp 5679\u20135681","DOI":"10.1609\/aaai.v34i04.5984"},{"key":"1279_CR20","doi-asserted-by":"crossref","unstructured":"Du L, Wang Y, Song G, et\u00a0al (2018a) Dynamic network embedding: an extended approach for skip-gram based network embedding. In: Proceedings of the 27th AAAI conference on artificial intelligence. AAAI Press, Palo Alto, CA, IJCAI\u201918, pp 2086\u20132092","DOI":"10.24963\/ijcai.2018\/288"},{"key":"1279_CR21","doi-asserted-by":"crossref","unstructured":"Du L, Wang Y, Song G, et\u00a0al (2018b) Dynamic network embedding: an extended approach for skip-gram based network embedding. In: Proceedings of the 27th international joint conference on artificial intelligence. AAAI Press, IJCAI\u201918, pp 2086\u20132092","DOI":"10.24963\/ijcai.2018\/288"},{"issue":"10","key":"1279_CR22","doi-asserted-by":"publisher","first-page":"4826","DOI":"10.1109\/TKDE.2020.3046511","volume":"34","author":"C Hou","year":"2022","unstructured":"Hou C, Zhang H, He S et al (2022) GloDyNE: global topology preserving dynamic network embedding. IEEE Trans Knowl Data Eng 34(10):4826\u20134837","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1279_CR23","unstructured":"Xu D, Ruan C, Korpeoglu E, et\u00a0al (2020) Inductive representation learning on temporal graphs. In: Proceedings of international conference on learning representations. OpenReview.net, Ithaca, NY, https:\/\/openreview.net\/forum?id=rJeW1yHYwH"},{"key":"1279_CR24","doi-asserted-by":"publisher","DOI":"10.1145\/3551892","author":"X Jiang","year":"2023","unstructured":"Jiang X, Yu Z, Hai C et al (2023) DNformer: temporal link prediction with transfer learning in dynamic networks. ACM Trans Knowl Discove Data. https:\/\/doi.org\/10.1145\/3551892","journal-title":"ACM Trans Knowl Discove Data"},{"key":"1279_CR25","doi-asserted-by":"publisher","unstructured":"Xu K, Hu W, Leskovec J, et\u00a0al (2019) How powerful are graph neural networks? In: International conference on learning representations https:\/\/doi.org\/10.48550\/arXiv.1810.00826","DOI":"10.48550\/arXiv.1810.00826"},{"issue":"1","key":"1279_CR26","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1115\/1.3653115","volume":"86","author":"RE Kalman","year":"1964","unstructured":"Kalman RE (1964) When is a linear control system optimal? J Basic Eng 86(1):51\u201360. https:\/\/doi.org\/10.1115\/1.3653115","journal-title":"J Basic Eng"},{"key":"1279_CR27","unstructured":"Ng AY, Russell SJ (2000) Algorithms for inverse reinforcement learning. In: Proceedings of the seventeenth international conference on machine learning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, ICML \u201900, pp 663\u2013670"},{"key":"1279_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.robot.2019.01.003","volume":"114","author":"C You","year":"2019","unstructured":"You C, Lu J, Filev D et al (2019) Advanced planning for autonomous vehicles using reinforcement learning and deep inverse reinforcement learning. Robot Auton Syst 114:1\u201318. https:\/\/doi.org\/10.1016\/j.robot.2019.01.003","journal-title":"Robot Auton Syst"},{"key":"1279_CR29","doi-asserted-by":"crossref","unstructured":"Ziebart BD, Maas AL, Dey AK, et\u00a0al (2008a) Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior. In: Proceedings of the 10th international conference on ubiquitous computing. Association for Computing Machinery, New York, UbiComp \u201908, pp 322\u2013331","DOI":"10.1145\/1409635.1409678"},{"key":"1279_CR30","unstructured":"Ziebart BD, Maas AL, Bagnell JA, et\u00a0al (2008b) Maximum entropy inverse reinforcement learning. In: Proceedings of the 23rd AAAI conference on artificial intelligence. Association for the Advancement of Artificial Intelligence, New York, pp 1433\u20131438"},{"key":"1279_CR31","volume-title":"Theory of games and economic behavior","author":"O Morgenstern","year":"1953","unstructured":"Morgenstern O, Von Neumann J (1953) Theory of games and economic behavior. Princeton University Press, Princeton"},{"issue":"9","key":"1279_CR32","doi-asserted-by":"publisher","first-page":"2163","DOI":"10.1016\/j.laa.2009.01.006","volume":"432","author":"W So","year":"2010","unstructured":"So W, Robbiano M, de Abreu NMM et al (2010) Applications of a theorem by Ky Fan in the theory of graph energy. Linear Algebra Appl 432(9):2163\u20132169. https:\/\/doi.org\/10.1016\/j.laa.2009.01.006","journal-title":"Linear Algebra Appl"},{"issue":"1","key":"1279_CR33","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1109\/TMC.2019.2938509","volume":"20","author":"CH Liu","year":"2021","unstructured":"Liu CH, Dai Z, Zhao Y et al (2021) Distributed and energy-efficient mobile crowdsensing with charging stations by deep reinforcement learning. IEEE Trans Mob Comput 20(1):130\u2013146. https:\/\/doi.org\/10.1109\/TMC.2019.2938509","journal-title":"IEEE Trans Mob Comput"},{"key":"1279_CR34","unstructured":"Vaswani A, Shazeer N, Parmar N, et\u00a0al (2017) Attention is all you need. In: Proceedings of the 31st international conference on neural information processing systems. Curran Associates Inc., Red Hook, NIPS\u201917, pp 6000\u20136010"},{"key":"1279_CR35","doi-asserted-by":"crossref","unstructured":"Rossi RA, Ahmed NK (2015) The network data repository with interactive graph analytics and visualization. In: Bonet B, Koenig S (eds) Proceedings of the 29th AAAI conference on artificial intelligence, January 25-30, 2015, Austin, Texas. AAAI Press, pp 4292\u20134293","DOI":"10.1609\/aaai.v29i1.9277"},{"key":"1279_CR36","unstructured":"Adler J, Lunz S (2018) Banach wasserstein gan. In: Proceedings of the 32nd international conference on neural information processing systems. Curran Associates Inc., Red Hook, NIPS\u201918, pp 6755\u20136764"},{"key":"1279_CR37","doi-asserted-by":"crossref","unstructured":"Junuthula RR, Xu KS, Devabhaktuni VK (2016) Evaluating link prediction accuracy in dynamic networks with added and removed edges. In: 2016 IEEE international conferences on big data and cloud computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom), pp 377\u2013384","DOI":"10.1109\/BDCloud-SocialCom-SustainCom.2016.63"},{"issue":"8","key":"1279_CR38","doi-asserted-by":"publisher","first-page":"4011","DOI":"10.1109\/TKDE.2020.3026311","volume":"34","author":"C Pu","year":"2022","unstructured":"Pu C, Li J, Wang J et al (2022) The node-similarity distribution of complex networks and its applications in link prediction. IEEE Trans Knowl Data Eng 34(8):4011\u20134023","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1279_CR39","doi-asserted-by":"crossref","unstructured":"Perozzi B, Al-Rfou R, Skiena S (2014) DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining. Association for Computing Machinery, New York, pp 701\u2013710","DOI":"10.1145\/2623330.2623732"}],"container-title":["Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-024-01279-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00607-024-01279-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-024-01279-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T18:11:34Z","timestamp":1717092694000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00607-024-01279-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,2]]},"references-count":39,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["1279"],"URL":"https:\/\/doi.org\/10.1007\/s00607-024-01279-w","relation":{},"ISSN":["0010-485X","1436-5057"],"issn-type":[{"type":"print","value":"0010-485X"},{"type":"electronic","value":"1436-5057"}],"subject":[],"published":{"date-parts":[[2024,4,2]]},"assertion":[{"value":"17 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 April 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}