{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T01:14:01Z","timestamp":1790385241602,"version":"4.1.0"},"publisher-location":"New York, NY, USA","reference-count":49,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Key Research Project of Zhejiang Province","award":["2022C01145"],"award-info":[{"award-number":["2022C01145"]}]},{"DOI":"10.13039\/501100006374","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62302451"],"award-info":[{"award-number":["62302451"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006374","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LQ22F020018"],"award-info":[{"award-number":["LQ22F020018"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of China","award":["62276233"],"award-info":[{"award-number":["62276233"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,5,13]]},"DOI":"10.1145\/3589334.3645432","type":"proceedings-article","created":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T07:08:13Z","timestamp":1715152093000},"page":"527-538","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["TATKC: A Temporal Graph Neural Network for Fast Approximate Temporal Katz Centrality Ranking"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3334-1701","authenticated-orcid":false,"given":"Tianming","family":"Zhang","sequence":"first","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0674-6305","authenticated-orcid":false,"given":"Junkai","family":"Fang","sequence":"additional","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1772-6863","authenticated-orcid":false,"given":"Zhengyi","family":"Yang","sequence":"additional","affiliation":[{"name":"University of New South Wales, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1062-6309","authenticated-orcid":false,"given":"Bin","family":"Cao","sequence":"additional","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0140-7043","authenticated-orcid":false,"given":"Jing","family":"Fan","sequence":"additional","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,5,13]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASONAM.2012.79"},{"key":"e_1_3_2_2_2_1","volume-title":"A mathematical model for group structures. Human organization","author":"Bavelas Alex","year":"1948","unstructured":"Alex Bavelas. 1948. A mathematical model for group structures. Human organization, Vol. 7, 3 (1948), 16--30."},{"key":"e_1_3_2_2_3_1","volume-title":"Temporal walk based centrality metric for graph streams. Applied network science","author":"B\u00e9res Ferenc","year":"2018","unstructured":"Ferenc B\u00e9res, R\u00f3bert P\u00e1lovics, Anna Ol\u00e1h, and Andr\u00e1s A Bencz\u00far. 2018. Temporal walk based centrality metric for graph streams. Applied network science, Vol. 3, 1 (2018), 1--26."},{"key":"e_1_3_2_2_4_1","volume-title":"2015 Proceedings of the Seventeenth Workshop on Algorithm Engineering and Experiments (ALENEX). SIAM, 133--146","author":"Bergamini Elisabetta","year":"2014","unstructured":"Elisabetta Bergamini, Henning Meyerhenke, and Christian L Staudt. 2014. Approximating betweenness centrality in large evolving networks. In 2015 Proceedings of the Seventeenth Workshop on Algorithm Engineering and Experiments (ALENEX). SIAM, 133--146."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3284359"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/0022250X.2001.9990249"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0218127407018403"},{"key":"e_1_3_2_2_8_1","volume-title":"The anatomy of a large-scale hypertextual web search engine. Computer networks and ISDN systems","author":"Brin Sergey","year":"1998","unstructured":"Sergey Brin and Lawrence Page. 1998. The anatomy of a large-scale hypertextual web search engine. Computer networks and ISDN systems, Vol. 30, 1--7 (1998), 107--117."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/1013367.1013537"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403259"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2660460.2660465"},{"key":"e_1_3_2_2_12_1","volume-title":"BAVARIAN: betweenness centrality approximation with variance-aware rademacher averages. ACM Transactions on Knowledge Discovery from Data","author":"Cousins Cyrus","year":"2023","unstructured":"Cyrus Cousins, Chloe Wohlgemuth, and Matteo Riondato. 2023. BAVARIAN: betweenness centrality approximation with variance-aware rademacher averages. ACM Transactions on Knowledge Discovery from Data, Vol. 17, 6 (2023), 1--47."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-35668-1_2"},{"key":"e_1_3_2_2_14_1","volume-title":"20th International Congress on Modelling and Simulation (MODSIM2013)","author":"Dekker AH","year":"2013","unstructured":"AH Dekker. 2013. Network centrality and super-spreaders in infectious disease epidemiology. In 20th International Congress on Modelling and Simulation (MODSIM2013). Citeseer, 331--337."},{"key":"e_1_3_2_2_15_1","volume-title":"Proceedings of the 2015 SIAM International Conference on Data Mining. SIAM, 433--441","author":"D\u00f3ra","year":"2015","unstructured":"D\u00f3ra ErdHo s, Vatche Ishakian, Azer Bestavros, and Evimaria Terzi. 2015. A divide-and-conquer algorithm for betweenness centrality. In Proceedings of the 2015 SIAM International Conference on Data Mining. SIAM, 433--441."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357979"},{"key":"e_1_3_2_2_17_1","volume-title":"A set of measures of centrality based on betweenness. Sociometry","author":"Freeman Linton C","year":"1977","unstructured":"Linton C Freeman. 1977. A set of measures of centrality based on betweenness. Sociometry (1977), 35--41."},{"key":"e_1_3_2_2_18_1","first-page":"238","article-title":"Centrality in social networks: Conceptual clarification. Social network: critical concepts in sociology. Londres","volume":"1","author":"Freeman Linton C","year":"2002","unstructured":"Linton C Freeman et al. 2002. Centrality in social networks: Conceptual clarification. Social network: critical concepts in sociology. Londres: Routledge , Vol. 1 (2002), 238--263.","journal-title":"Routledge"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3237192"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2015.7280334"},{"key":"e_1_3_2_2_21_1","volume-title":"Using Causality-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic Graphs. arXiv preprint arXiv:2310.15865","author":"Heeg Franziska","year":"2023","unstructured":"Franziska Heeg and Ingo Scholtes. 2023. Using Causality-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic Graphs. arXiv preprint arXiv:2310.15865 (2023)."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02289026"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.85.026107"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1966.10480879"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-012-0076-6"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-013-0143-7"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2567948.2577358"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/1830252.1830262"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-38961-1_50"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1093\/comnet\/cnt001"},{"key":"e_1_3_2_2_31_1","volume-title":"Graph neural networks for fast node ranking approximation. ACM Transactions on Knowledge Discovery from Data (TKDD)","author":"Maurya Sunil Kumar","year":"2021","unstructured":"Sunil Kumar Maurya, Xin Liu, and Tsuyoshi Murata. 2021. Graph neural networks for fast node ranking approximation. ACM Transactions on Knowledge Discovery from Data (TKDD), Vol. 15, 5 (2021), 1--32."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2017.05.021"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"crossref","unstructured":"MEJ Newman. 2010. 2010Networks: an introduction. bibinfonumpages167--193 pages.","DOI":"10.1016\/j.cosust.2010.04.004"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00049"},{"key":"e_1_3_2_2_35_1","volume-title":"Computing top-k temporal closeness in temporal networks. Knowledge and Information Systems","author":"Oettershagen Lutz","year":"2022","unstructured":"Lutz Oettershagen and Petra Mutzel. 2022. Computing top-k temporal closeness in temporal networks. Knowledge and Information Systems (2022), 1--29."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512210"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.23919\/ACC.2017.7963730"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2014.6816651"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/2556195.2556224"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3208351"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46227-1_42"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512204"},{"key":"e_1_3_2_2_43_1","volume-title":"Graph manipulations for fast centrality computation. ACM Transactions on Knowledge Discovery from Data (TKDD)","author":"Sariy\u00fcce Ahmet Erdem","year":"2017","unstructured":"Ahmet Erdem Sariy\u00fcce, Kamer Kaya, Erik Saule, and \u00dcmit V cC ataly\u00fcrek. 2017. Graph manipulations for fast centrality computation. ACM Transactions on Knowledge Discovery from Data (TKDD), Vol. 11, 3 (2017), 1--25."},{"key":"e_1_3_2_2_44_1","volume-title":"Centrality measures in complex networks: A survey. arXiv preprint arXiv:2011.07190","author":"Saxena Akrati","year":"2020","unstructured":"Akrati Saxena and Sudarshan Iyengar. 2020. Centrality measures in complex networks: A survey. arXiv preprint arXiv:2011.07190 (2020)."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41060-019-00189-x"},{"key":"e_1_3_2_2_46_1","volume-title":"Attention is all you need. Advances in neural information processing systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems , Vol. 30 (2017)."},{"key":"e_1_3_2_2_47_1","volume-title":"Fast approximation of centrality. Graph algorithms and applications","author":"Joseph Wang David Eppstein","year":"2006","unstructured":"David Eppstein Joseph Wang. 2006. Fast approximation of centrality. Graph algorithms and applications, Vol. 5, 5 (2006), 39."},{"key":"e_1_3_2_2_48_1","volume-title":"Inductive representation learning on temporal graphs. arXiv preprint arXiv:2002.07962","author":"Xu Da","year":"2020","unstructured":"Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan. 2020. Inductive representation learning on temporal graphs. arXiv preprint arXiv:2002.07962 (2020)."},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cnsns.2019.01.032"}],"event":{"name":"WWW '24: The ACM Web Conference 2024","location":"Singapore Singapore","acronym":"WWW '24","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2024"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589334.3645432","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3589334.3645432","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:31:32Z","timestamp":1755822692000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589334.3645432"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,13]]},"references-count":49,"alternative-id":["10.1145\/3589334.3645432","10.1145\/3589334"],"URL":"https:\/\/doi.org\/10.1145\/3589334.3645432","relation":{},"subject":[],"published":{"date-parts":[[2024,5,13]]},"assertion":[{"value":"2024-05-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}