{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T23:49:55Z","timestamp":1779234595650,"version":"3.51.4"},"reference-count":73,"publisher":"Elsevier BV","issue":"6","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100009015","name":"Liaoning Technical University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100009015","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Processing &amp; Management"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.ipm.2026.104702","type":"journal-article","created":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T22:50:20Z","timestamp":1772923820000},"page":"104702","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"title":["Learning to rank critical road segments via heterogeneous graphs with origin-destination flow integration"],"prefix":"10.1016","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8849-9656","authenticated-orcid":false,"given":"Ming","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinrong","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4436-5322","authenticated-orcid":false,"given":"Zilong","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangfu","family":"Meng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.ipm.2026.104702_bib0001","series-title":"Proceedings of the eleventh ACM conference on recommender systems","first-page":"42","article-title":"Controlling popularity bias in learning-to-rank recommendation","author":"Abdollahpouri","year":"2017"},{"key":"10.1016\/j.ipm.2026.104702_bib0002","doi-asserted-by":"crossref","first-page":"2186","DOI":"10.1016\/j.amc.2012.08.064","article-title":"An algorithm for ranking the nodes of an urban network based on the concept of pagerank vector","volume":"219","author":"Agryzkov","year":"2012","journal-title":"Applied Mathematics and Computation"},{"key":"10.1016\/j.ipm.2026.104702_bib0003","series-title":"The 41st international ACM SIGIR conference on research & development in information retrieval","first-page":"135","article-title":"Learning a deep listwise context model for ranking refinement","author":"Ai","year":"2018"},{"key":"10.1016\/j.ipm.2026.104702_bib0004","series-title":"Proceedings of the 32nd ACM international conference on information and knowledge management","first-page":"4502","article-title":"Regression compatible listwise objectives for calibrated ranking with binary relevance","author":"Bai","year":"2023"},{"key":"10.1016\/j.ipm.2026.104702_bib0005","unstructured":"Brody, S., Alon, U., & Yahav, E. (2021). How attentive are graph attention networks?arXiv: 2105.14491."},{"key":"10.1016\/j.ipm.2026.104702_bib0006","series-title":"Proceedings of the 22nd international conference on machine learning","first-page":"89","article-title":"Learning to rank using gradient descent","author":"Burges","year":"2005"},{"key":"10.1016\/j.ipm.2026.104702_bib0007","series-title":"Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining","first-page":"3762-3773","article-title":"RankFormer: Listwise learning-to-rank using listwide labels","author":"Buyl","year":"2023"},{"key":"10.1016\/j.ipm.2026.104702_bib0008","series-title":"Proceedings of the 24th international conference on machine learning","first-page":"129-136","article-title":"Learning to rank: From pairwise approach to listwise approach","author":"Cao","year":"2007"},{"key":"10.1016\/j.ipm.2026.104702_bib0009","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.107611","article-title":"Megnn: Meta-path extracted graph neural network for heterogeneous graph representation learning","volume":"235","author":"Chang","year":"2022","journal-title":"Knowledge-based Systems"},{"key":"10.1016\/j.ipm.2026.104702_bib0010","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv: 1406.1078.","DOI":"10.3115\/v1\/D14-1179"},{"key":"10.1016\/j.ipm.2026.104702_bib0011","series-title":"2012\u202fIEEE 12th international conference on data mining","first-page":"181","article-title":"Link prediction and recommendation across heterogeneous social networks","author":"Dong","year":"2012"},{"key":"10.1016\/j.ipm.2026.104702_bib0012","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1038\/s42256-020-0177-2","article-title":"Finding key players in complex networks through deep reinforcement learning","volume":"2","author":"Fan","year":"2020","journal-title":"Nature Machine Intelligence"},{"issue":"1","key":"10.1016\/j.ipm.2026.104702_bib0013","doi-asserted-by":"crossref","first-page":"35","DOI":"10.2307\/3033543","article-title":"A set of measures of centrality based on betweenness","volume":"40","author":"Freeman","year":"1977","journal-title":"Sociometry"},{"key":"10.1016\/j.ipm.2026.104702_bib0014","series-title":"Proceedings of the web conference 2020","first-page":"2331","article-title":"Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding","author":"Fu","year":"2020"},{"key":"10.1016\/j.ipm.2026.104702_bib0015","series-title":"Long short-term memory","first-page":"37","author":"Graves","year":"2012"},{"key":"10.1016\/j.ipm.2026.104702_bib0016","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.110930","article-title":"HMSG: Heterogeneous graph neural network based on metapath subgraph learning","volume":"279","author":"Guan","year":"2023","journal-title":"Knowledge-based Systems"},{"key":"10.1016\/j.ipm.2026.104702_bib0017","doi-asserted-by":"crossref","DOI":"10.1016\/j.jtrangeo.2024.104080","article-title":"An adaptive OD flow clustering method to identify heterogeneous urban mobility trends","volume":"123","author":"Guo","year":"2025","journal-title":"Journal of Transport Geography"},{"key":"10.1016\/j.ipm.2026.104702_bib0018","series-title":"Proceedings of the 30th conference on neural information processing systems (neurIPS)","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume":"vol. 30","author":"Hamilton","year":"2017"},{"key":"10.1016\/j.ipm.2026.104702_bib0019","series-title":"Proceedings of the 28th ACM international conference on information and knowledge management","first-page":"639","article-title":"HeteSpaceyWalk: A heterogeneous spacey random walk for heterogeneous information network embedding","author":"He","year":"2019"},{"key":"10.1016\/j.ipm.2026.104702_bib0020","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"4132","article-title":"An attention-based graph neural network for heterogeneous structural learning","volume":"vol. 34","author":"Hong","year":"2020"},{"key":"10.1016\/j.ipm.2026.104702_bib0021","series-title":"Proceedings of the web conference 2020","first-page":"2704-2710","article-title":"Heterogeneous graph transformer","author":"Hu","year":"2020"},{"key":"10.1016\/j.ipm.2026.104702_bib0022","doi-asserted-by":"crossref","first-page":"16622","DOI":"10.1109\/TITS.2022.3163756","article-title":"Traffic node importance evaluation based on clustering in represented transportation networks","volume":"23","author":"Huang","year":"2022","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"10.1016\/j.ipm.2026.104702_bib0023","series-title":"Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining","first-page":"732","article-title":"Graph recurrent networks with attributed random walks","author":"Huang","year":"2019"},{"key":"10.1016\/j.ipm.2026.104702_bib0024","series-title":"Proceedings of the 27th ACM international conference on information and knowledge management","first-page":"437","article-title":"Are meta-paths necessary? revisiting heterogeneous graph embeddings","author":"Hussein","year":"2018"},{"key":"10.1016\/j.ipm.2026.104702_bib0025","series-title":"Proceedings of the 35th international conference on machine learning","first-page":"2127","article-title":"Attention-based deep multiple instance learning","volume":"vol. 80","author":"Ilse","year":"2018"},{"issue":"12","key":"10.1016\/j.ipm.2026.104702_bib0026","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0296045","article-title":"Edge-based graph neural network for ranking critical road segments in a network","volume":"18","author":"Jana","year":"2023","journal-title":"PloS One"},{"issue":"4","key":"10.1016\/j.ipm.2026.104702_bib0027","doi-asserted-by":"crossref","first-page":"4365","DOI":"10.1609\/aaai.v37i4.25556","article-title":"PDFormer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction","volume":"37","author":"Jiang","year":"2023","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"10.1016\/j.ipm.2026.104702_bib0028","series-title":"Advances in neural information processing systems","article-title":"LightGBM: A highly efficient gradient boosting decision tree","volume":"vol. 30","author":"Ke","year":"2017"},{"issue":"1\u20132","key":"10.1016\/j.ipm.2026.104702_bib0029","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1093\/biomet\/30.1-2.81","article-title":"A new measure of rank correlation","volume":"30","author":"KENDALL","year":"1938","journal-title":"Biometrika"},{"key":"10.1016\/j.ipm.2026.104702_bib0030","series-title":"Proceedings of the 26th annual international conference on machine learning","first-page":"577","article-title":"Generalization analysis of listwise learning-to-rank algorithms","author":"Lan","year":"2009"},{"key":"10.1016\/j.ipm.2026.104702_bib0031","series-title":"Proceedings of the 36th international conference on machine learning","first-page":"3744","article-title":"Set transformer: A framework for attention-based permutation-invariant neural networks","volume":"vol. 97","author":"Lee","year":"2019"},{"key":"10.1016\/j.ipm.2026.104702_bib0032","first-page":"897","article-title":"McRank: Learning to rank using multiple classification and gradient boosting","volume":"20","author":"Li","year":"2007","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.ipm.2026.104702_bib0033","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"147","article-title":"Type-aware anchor link prediction across heterogeneous networks based on graph attention network","volume":"vol. 34","author":"Li","year":"2020"},{"issue":"9","key":"10.1016\/j.ipm.2026.104702_bib0034","doi-asserted-by":"crossref","DOI":"10.3390\/math13091458","article-title":"A dynamic regional-aggregation-based heterogeneous graph neural network for traffic prediction","volume":"13","author":"Liu","year":"2025","journal-title":"Mathematics"},{"key":"10.1016\/j.ipm.2026.104702_bib0035","series-title":"Proceedings of the 21st international conference on intelligent transportation systems (ITSC)","first-page":"2575","article-title":"Microscopic traffic simulation using SUMO","author":"Lopez","year":"2018"},{"key":"10.1016\/j.ipm.2026.104702_bib0036","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3446217","article-title":"Graph neural networks for fast node ranking approximation","volume":"15","author":"Maurya","year":"2021","journal-title":"ACM Transactions on Knowledge Discovery from Data (TKDD)"},{"key":"10.1016\/j.ipm.2026.104702_bib0037","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.126751","article-title":"Two-level attention mechanism with contrastive learning for heterogeneous graph representation learning","volume":"273","author":"Moradi","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.ipm.2026.104702_bib0038","series-title":"Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining","first-page":"701","article-title":"DeepWalk: Online learning of social representations","author":"Perozzi","year":"2014"},{"key":"10.1016\/j.ipm.2026.104702_bib0039","unstructured":"Pobrotyn, P., Bartczak, T., Synowiec, M., Bia\u0142obrzeski, R., & Bojar, J. (2020). Context-aware learning to rank with self-attention. arXiv: 2005.10084."},{"key":"10.1016\/j.ipm.2026.104702_bib0040","series-title":"Proceedings of the 3rd annual learning on graphs conference (log 2024)","first-page":"25:1","article-title":"Edge directionality improves learning on heterophilic graphs","volume":"vol. 214","author":"Rossi","year":"2024"},{"key":"10.1016\/j.ipm.2026.104702_bib0041","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.126292","article-title":"GLC: A dual-perspective approach for identifying influential nodes in complex networks","volume":"268","author":"Ruan","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.ipm.2026.104702_bib0042","series-title":"Companion proceedings of the web conference 2022","first-page":"1157","article-title":"SchemaWalk: Schema aware random walks for heterogeneous graph embedding","author":"Samy","year":"2022"},{"key":"10.1016\/j.ipm.2026.104702_bib0043","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1109\/TKDE.2018.2833443","article-title":"Heterogeneous information network embedding for recommendation","volume":"31","author":"Shi","year":"2018","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.1016\/j.ipm.2026.104702_bib0044","series-title":"Proceedings of the 2024 conference on empirical methods in natural language processing: Industry track","first-page":"1152","article-title":"DiAL: Diversity aware listwise ranking for query auto-complete","author":"Singh","year":"2024"},{"key":"10.1016\/j.ipm.2026.104702_bib0045","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.neucom.2017.02.097","article-title":"Stock portfolio selection using learning-to-rank algorithms with news sentiment","volume":"264","author":"Song","year":"2017","journal-title":"Neurocomputing"},{"key":"10.1016\/j.ipm.2026.104702_bib0046","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.120927","article-title":"Finding critical nodes in a complex network from information diffusion and Matthew effect aggregation","volume":"233","author":"Sun","year":"2023","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.ipm.2026.104702_bib0047","doi-asserted-by":"crossref","first-page":"3772","DOI":"10.1109\/TASE.2024.3399322","article-title":"Learning to detect critical nodes in sparse graphs via feature importance awareness","volume":"22","author":"Tan","year":"2024","journal-title":"IEEE Transactions on Automation Science and Engineering"},{"key":"10.1016\/j.ipm.2026.104702_bib0048","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision","first-page":"8273","article-title":"Learning to rank proposals for object detection","author":"Tan","year":"2019"},{"key":"10.1016\/j.ipm.2026.104702_bib0049","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"8494","article-title":"Listwise learning to rank based on approximate rank indicators","volume":"vol. 36","author":"Thonet","year":"2022"},{"key":"10.1016\/j.ipm.2026.104702_bib0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102439","article-title":"Towards investigating influencers in complex social networks using electric potential concept from a centrality perspective","volume":"109","author":"Ullah","year":"2024","journal-title":"Information Fusion"},{"key":"10.1016\/j.ipm.2026.104702_bib0051","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2025.104201","article-title":"OVED-Rank: A ranking scheme to evaluate complex network spreaders\u2019 influence through the concept of effective distance and orbital velocity","volume":"62","author":"Ullah","year":"2025","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2026.104702_bib0052","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.120326","article-title":"LSS: A locality-based structure system to evaluate the spreader\u2019s importance in social complex networks","volume":"228","author":"Ullah","year":"2023","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.ipm.2026.104702_bib0053","series-title":"Advances in neural information processing systems","article-title":"Attention is all you need","volume":"vol. 30","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.ipm.2026.104702_bib0054","series-title":"Proceedings of the 6th international conference on learning representations (ICLR)","article-title":"Graph attention networks","author":"Velickovic","year":"2018"},{"key":"10.1016\/j.ipm.2026.104702_bib0055","unstructured":"Veli\u010dkovi\u0107, P., Fedus, W., Hamilton, W. L., Li\u00f2, P., Bengio, Y., & Hjelm, R. D. (2018). Deep graph infomax. arXiv: 1809.10341."},{"issue":"4","key":"10.1016\/j.ipm.2026.104702_bib0056","doi-asserted-by":"crossref","first-page":"5496","DOI":"10.1109\/TCSS.2024.3372856","article-title":"Traffic origin-destination demand prediction via multichannel hypergraph convolutional networks","volume":"11","author":"Wang","year":"2024","journal-title":"IEEE Transactions on Computational Social Systems"},{"key":"10.1016\/j.ipm.2026.104702_bib0057","series-title":"Proc. world wide web conf.","first-page":"2022","article-title":"Heterogeneous graph attention network","author":"Wang","year":"2019"},{"key":"10.1016\/j.ipm.2026.104702_bib0058","series-title":"Proceedings of the 27th ACM international conference on information and knowledge management","first-page":"1313-1322","article-title":"The lambdaloss framework for ranking metric optimization","author":"Wang","year":"2018"},{"key":"10.1016\/j.ipm.2026.104702_bib0059","doi-asserted-by":"crossref","first-page":"1934","DOI":"10.1109\/TKDE.2016.2535214","article-title":"Listwise learning to rank by exploring structure of objects","volume":"28","author":"Wu","year":"2016","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.1016\/j.ipm.2026.104702_bib0060","series-title":"Proceedings of the 42nd international conference on machine learning (ICML)","article-title":"When do LLMs help with node classification? A comprehensive analysis","author":"Wu","year":"2025"},{"key":"10.1016\/j.ipm.2026.104702_bib0061","series-title":"Proceedings of the 25th international conference on machine learning","first-page":"1192","article-title":"Listwise approach to learning to rank: Theory and algorithm","author":"Xia","year":"2008"},{"key":"10.1016\/j.ipm.2026.104702_bib0062","series-title":"Proceedings of the 25th international conference on machine learning","first-page":"1192","article-title":"Listwise approach to learning to rank: Theory and algorithm","author":"Xia","year":"2008"},{"key":"10.1016\/j.ipm.2026.104702_bib0063","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.102950","article-title":"Short-term OD flow prediction for urban rail transit control: A multi-graph spatiotemporal fusion approach","volume":"118","author":"Xing","year":"2025","journal-title":"Information Fusion"},{"key":"10.1016\/j.ipm.2026.104702_bib0064","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2025.104232","article-title":"AFR-Rank: An effective and highly efficient LLM-based listwise reranking framework via filtering noise documents","volume":"62","author":"Xiong","year":"2025","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2026.104702_bib0065","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/TITS.2018.2817282","article-title":"Discovery of critical nodes in road networks through mining from vehicle trajectories","volume":"20","author":"Xu","year":"2018","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"10.1016\/j.ipm.2026.104702_bib0066","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2024.120472","article-title":"MGL2Rank: Learning to rank the importance of nodes in road networks based on multi-graph fusion","volume":"667","author":"Xu","year":"2024","journal-title":"Information Sciences"},{"issue":"4","key":"10.1016\/j.ipm.2026.104702_bib0067","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2024.103763","article-title":"Higher-order embedded learning for heterogeneous information networks and adaptive POI recommendation","volume":"61","author":"Xun","year":"2024","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2026.104702_bib0068","doi-asserted-by":"crossref","unstructured":"Yang, S., Bi, K., Cui, W., Guo, J., & Cheng, X. (2024). Linkage: Listwise ranking among varied-quality references for non-factoid QA evaluation via LLMs. arXiv: 2409.14744.","DOI":"10.18653\/v1\/2024.findings-emnlp.410"},{"issue":"9","key":"10.1016\/j.ipm.2026.104702_bib0069","doi-asserted-by":"crossref","first-page":"10816","DOI":"10.1609\/aaai.v37i9.26283","article-title":"Simple and efficient heterogeneous graph neural network","volume":"37","author":"Yang","year":"2023","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"10.1016\/j.ipm.2026.104702_bib0070","first-page":"3391","article-title":"Deep sets","volume":"30","author":"Zaheer","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.ipm.2026.104702_bib0071","series-title":"Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining","first-page":"793","article-title":"Heterogeneous graph neural network","author":"Zhang","year":"2019"},{"key":"10.1016\/j.ipm.2026.104702_bib0072","series-title":"Proceedings of the 13th international conference on web search and data mining","first-page":"798","article-title":"Listwise learning to rank by exploring unique ratings","author":"Zhu","year":"2020"},{"key":"10.1016\/j.ipm.2026.104702_bib0073","series-title":"Proceedings of the 33rd international joint conference on artificial intelligence (IJCAI)","article-title":"Efficient tuning and inference for large language models on textual graphs","author":"Zhu","year":"2024"}],"container-title":["Information Processing &amp; Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0306457326000932?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0306457326000932?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T13:17:07Z","timestamp":1774012627000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0306457326000932"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":73,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,9]]}},"alternative-id":["S0306457326000932"],"URL":"https:\/\/doi.org\/10.1016\/j.ipm.2026.104702","relation":{},"ISSN":["0306-4573"],"issn-type":[{"value":"0306-4573","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Learning to rank critical road segments via heterogeneous graphs with origin-destination flow integration","name":"articletitle","label":"Article Title"},{"value":"Information Processing & Management","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ipm.2026.104702","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104702"}}