{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:22:46Z","timestamp":1771024966084,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":33,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"the National Natural Science Foundation of China","award":["62272023, 51991391"],"award-info":[{"award-number":["62272023, 51991391"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["YWF-23-L-1203"],"award-info":[{"award-number":["YWF-23-L-1203"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3672060","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:54:55Z","timestamp":1724561695000},"page":"1257-1268","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["MemMap: An Adaptive and Latent Memory Structure for Dynamic Graph Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8753-5475","authenticated-orcid":false,"given":"Shuo","family":"Ji","sequence":"first","affiliation":[{"name":"CCSE Lab, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2906-4289","authenticated-orcid":false,"given":"Mingzhe","family":"Liu","sequence":"additional","affiliation":[{"name":"CCSE Lab, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0157-1716","authenticated-orcid":false,"given":"Leilei","family":"Sun","sequence":"additional","affiliation":[{"name":"CCSE Lab, Beihang University & Key Laboratory of Data Science and Intelligent Computing, International Innovation Institute, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9030-8495","authenticated-orcid":false,"given":"Chuanren","family":"Liu","sequence":"additional","affiliation":[{"name":"The University of Tennessee, Knoxville, TN, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8948-3103","authenticated-orcid":false,"given":"Tongyu","family":"Zhu","sequence":"additional","affiliation":[{"name":"CCSE Lab, Beihang University & Key Laboratory of Data Science and Intelligent Computing, International Innovation Institute, Beihang University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462968"},{"key":"e_1_3_2_2_2_1","volume-title":"Do We Really Need Complicated Model Architectures For Temporal Networks? arXiv preprint arXiv:2302.11636","author":"Cong Weilin","year":"2023","unstructured":"Weilin Cong, Si Zhang, Jian Kang, Baichuan Yuan, Hao Wu, Xin Zhou, Hanghang Tong, and Mehrdad Mahdavi. 2023. Do We Really Need Complicated Model Architectures For Temporal Networks? arXiv preprint arXiv:2302.11636 (2023)."},{"key":"e_1_3_2_2_3_1","unstructured":"Alexey Dosovitskiy Lucas Beyer Alexander Kolesnikov Dirk Weissenborn Xiaohua Zhai Thomas Unterthiner Mostafa Dehghani Matthias Minderer Georg Heigold Sylvain Gelly et al. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.06.024"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539273"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"crossref","unstructured":"Shuo Ji Xiaodong Lu Mingzhe Liu Leilei Sun Chuanren Liu Bowen Du and Hui Xiong. 2023. Community-based Dynamic Graph Learning for Popularity Prediction. ACM 930--940.","DOI":"10.1145\/3580305.3599281"},{"key":"e_1_3_2_2_7_1","first-page":"19874","article-title":"Neural temporal walks: Motif-aware representation learning on continuous-time dynamic graphs","volume":"35","author":"Jin Ming","year":"2022","unstructured":"Ming Jin, Yuan-Fang Li, and Shirui Pan. 2022. Neural temporal walks: Motif-aware representation learning on continuous-time dynamic graphs. Advances in Neural Information Processing Systems, Vol. 35 (2022), 19874--19886.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.5555\/3455716.3455786"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0925--2312(98)00030--7"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330895"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00041"},{"key":"e_1_3_2_2_12_1","volume-title":"Tseng","author":"Lin Yu-Ru","year":"2008","unstructured":"Yu-Ru Lin, Yun Chi, Shenghuo Zhu, Hari Sundaram, and Belle L. Tseng. 2008. Facetnet: a framework for analyzing communities and their evolutions in dynamic networks. ACM, 685--694."},{"key":"e_1_3_2_2_13_1","volume-title":"Learning on Graphs Conference. PMLR, 1--1.","author":"Luo Yuhong","year":"2022","unstructured":"Yuhong Luo and Pan Li. 2022. Neighborhood-aware scalable temporal network representation learning. In Learning on Graphs Conference. PMLR, 1--1."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401092"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3184558.3191526"},{"key":"e_1_3_2_2_16_1","volume-title":"A time series is worth 64 words: Long-term forecasting with transformers. arXiv preprint arXiv:2211.14730","author":"Nie Yuqi","year":"2022","unstructured":"Yuqi Nie, Nam H Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam. 2022. A time series is worth 64 words: Long-term forecasting with transformers. arXiv preprint arXiv:2211.14730 (2022)."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5984"},{"key":"e_1_3_2_2_18_1","volume-title":"Linguistic inquiry and word count: LIWC","author":"Pennebaker James W","year":"2001","unstructured":"James W Pennebaker, Martha E Francis, and Roger J Booth. 2001. Linguistic inquiry and word count: LIWC 2001. Mahway: Lawrence Erlbaum Associates, Vol. 71, 2001 (2001), 2001."},{"key":"e_1_3_2_2_19_1","volume-title":"Towards Better Evaluation for Dynamic Link Prediction. In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022","author":"Poursafaei Farimah","year":"2022","unstructured":"Farimah Poursafaei, Shenyang Huang, and Kellin Pelrine and. 2022. Towards Better Evaluation for Dynamic Link Prediction. In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022."},{"key":"e_1_3_2_2_20_1","volume-title":"Temporal graph networks for deep learning on dynamic graphs. arXiv preprint arXiv:2006.10637","author":"Rossi Emanuele","year":"2020","unstructured":"Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, and Michael Bronstein. 2020. Temporal graph networks for deep learning on dynamic graphs. arXiv preprint arXiv:2006.10637 (2020)."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371845"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE48307.2020.00178"},{"key":"e_1_3_2_2_23_1","unstructured":"Amauri H. Souza Diego Mesquita Samuel Kaski and Vikas Garg. 2022. Provably expressive temporal graph networks."},{"key":"e_1_3_2_2_24_1","volume-title":"7th International Conference on Learning Representations, ICLR 2019","author":"Trivedi Rakshit","year":"2019","unstructured":"Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, and Hongyuan Zha. 2019. DyRep: Learning Representations over Dynamic Graphs. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6--9, 2019. OpenReview.net."},{"key":"e_1_3_2_2_25_1","volume-title":"TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning. CoRR","author":"Wang Lu","year":"2021","unstructured":"Lu Wang, Xiaofu Chang, Shuang Li, Yunfei Chu, Hui Li, Wei Zhang, Xiaofeng He, Le Song, Jingren Zhou, and Hongxia Yang. 2021. TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning. CoRR, Vol. abs\/2105.07944 (2021). showeprint[arXiv]2105.07944 https:\/\/arxiv.org\/abs\/2105.07944"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457564"},{"key":"e_1_3_2_2_27_1","volume-title":"Inductive representation learning in temporal networks via causal anonymous walks. arXiv preprint arXiv:2101.05974","author":"Wang Yanbang","year":"2021","unstructured":"Yanbang Wang, Yen-Yu Chang, Yunyu Liu, Jure Leskovec, and Pan Li. 2021. Inductive representation learning in temporal networks via causal anonymous walks. arXiv preprint arXiv:2101.05974 (2021)."},{"key":"e_1_3_2_2_28_1","volume-title":"Principal component analysis. Chemometrics and intelligent laboratory systems","author":"Wold Svante","year":"1987","unstructured":"Svante Wold, Kim Esbensen, and Paul Geladi. 1987. Principal component analysis. Chemometrics and intelligent laboratory systems, Vol. 2, 1--3 (1987), 37--52."},{"key":"e_1_3_2_2_29_1","volume-title":"8th International Conference on Learning Representations, ICLR 2020","author":"Xu Da","year":"2020","unstructured":"Da Xu, Chuanwei Ruan, Evren K\u00f6rpeoglu, Sushant Kumar, and Kannan Achan. 2020. Inductive representation learning on temporal graphs. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26--30, 2020."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539300"},{"key":"e_1_3_2_2_31_1","volume-title":"Towards Better Dynamic Graph Learning: New Architecture and Unified Library. arXiv preprint arXiv:2303.13047","author":"Yu Le","year":"2023","unstructured":"Le Yu, Leilei Sun, Bowen Du, and Weifeng Lv. 2023. Towards Better Dynamic Graph Learning: New Architecture and Unified Library. arXiv preprint arXiv:2303.13047 (2023)."},{"key":"e_1_3_2_2_32_1","first-page":"4741","article-title":"Dynamic graph neural networks for sequential recommendation","volume":"35","author":"Zhang Mengqi","year":"2022","unstructured":"Mengqi Zhang, Shu Wu, Xueli Yu, Qiang Liu, and Liang Wang. 2022. Dynamic graph neural networks for sequential recommendation. IEEE Transactions on Knowledge and Data Engineering, Vol. 35, 5 (2022), 4741--4753.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_2_33_1","volume-title":"Dynamic graph learning based on hierarchical memory for origin-destination demand prediction. arXiv preprint arXiv:2205.14593","author":"Zhang Ruixing","year":"2022","unstructured":"Ruixing Zhang, Liangzhe Han, Boyi Liu, Jiayuan Zeng, and Leilei Sun. 2022. Dynamic graph learning based on hierarchical memory for origin-destination demand prediction. arXiv preprint arXiv:2205.14593 (2022)."}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Barcelona Spain","acronym":"KDD '24","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3672060","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3672060","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:23Z","timestamp":1750291463000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3672060"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":33,"alternative-id":["10.1145\/3637528.3672060","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3672060","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}