乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:35:44Z","timestamp":1740101744731,"version":"3.37.3"},"publisher-location":"New York, NY, USA","reference-count":60,"publisher":"ACM","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1936103, 61921003, 62202164"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,4,30]]},"DOI":"10.1145\/3543507.3583353","type":"proceedings-article","created":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T23:30:51Z","timestamp":1682551851000},"page":"360-371","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["SINCERE: Sequential Interaction Networks representation learning on Co-Evolving RiEmannian manifolds"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2900-4908","authenticated-orcid":false,"given":"Junda","family":"Ye","sequence":"first","affiliation":[{"name":"Beijing University of Posts and Telecommunications, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3242-150X","authenticated-orcid":false,"given":"Zhongbao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4562-2279","authenticated-orcid":false,"given":"Li","family":"Sun","sequence":"additional","affiliation":[{"name":"North China Electric Power University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9457-3015","authenticated-orcid":false,"given":"Yang","family":"Yan","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5074-1606","authenticated-orcid":false,"given":"Feiyang","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1234-0515","authenticated-orcid":false,"given":"Fuxin","family":"Ren","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, China"}]}],"member":"320","published-online":{"date-parts":[[2023,4,30]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2601412"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3524984"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3097997"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159727"},{"key":"e_1_3_2_1_5_1","volume-title":"Advances in Neural Information Processing Systems, H.\u00a0Larochelle, M.\u00a0Ranzato, R.\u00a0Hadsell, M.F. Balcan, and H.\u00a0Lin (Eds.). Vol.\u00a033. Curran Associates","author":"Brehmer Johann","year":"2020","unstructured":"Johann Brehmer and Kyle Cranmer. 2020. Flows for Simultaneous Manifold Learning and Density Estimation. In Advances in Neural Information Processing Systems, H.\u00a0Larochelle, M.\u00a0Ranzato, R.\u00a0Hadsell, M.F. Balcan, and H.\u00a0Lin (Eds.). Vol.\u00a033. Curran Associates, Inc., 442\u2013453. https:\/\/proceedings.neurips.cc\/paper\/2020\/file\/051928341be67dcba03f0e04104d9047-Paper.pdf"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441783"},{"key":"e_1_3_2_1_7_1","volume-title":"Scalable Hyperbolic Recommender Systems. arXiv preprint arXiv:1902.08648","author":"Chamberlain Benjamin\u00a0Paul","year":"2019","unstructured":"Benjamin\u00a0Paul Chamberlain, Stephen\u00a0R Hardwick, David\u00a0R Wardrope, Fabon Dzogang, Fabio Daolio, and Sa\u00fal Vargas. 2019. Scalable Hyperbolic Recommender Systems. arXiv preprint arXiv:1902.08648 (2019). https:\/\/arxiv.org\/pdf\/1902.08648.pdf"},{"key":"e_1_3_2_1_8_1","volume-title":"Advances in Neural Information Processing Systems, H.\u00a0Larochelle, M.\u00a0Ranzato, R.\u00a0Hadsell, M.F. Balcan, and H.\u00a0Lin (Eds.). Vol.\u00a033. Curran Associates","author":"Chami Ines","year":"2020","unstructured":"Ines Chami, Albert Gu, Vaggos Chatziafratis, and Christopher R\u00e9. 2020. From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering. In Advances in Neural Information Processing Systems, H.\u00a0Larochelle, M.\u00a0Ranzato, R.\u00a0Hadsell, M.F. Balcan, and H.\u00a0Lin (Eds.). Vol.\u00a033. Curran Associates, Inc., 15065\u201315076. https:\/\/proceedings.neurips.cc\/paper\/2020\/file\/ac10ec1ace51b2d973cd87973a98d3ab-Paper.pdf"},{"key":"e_1_3_2_1_9_1","volume-title":"Hyperbolic Graph Convolutional Neural Networks. Advances in Neural Information Processing Systems 32","author":"Chami Ines","year":"2019","unstructured":"Ines Chami, Zhitao Ying, Christopher R\u00e9, and Jure Leskovec. 2019. Hyperbolic Graph Convolutional Neural Networks. Advances in Neural Information Processing Systems 32 (2019)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449921"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-92273-3_27"},{"key":"e_1_3_2_1_12_1","volume-title":"Deep Coevolutionary Network: Embedding User and Item Features for Recommendation. arXiv preprint arXiv:1609.03675","author":"Dai Hanjun","year":"2016","unstructured":"Hanjun Dai, Yichen Wang, Rakshit Trivedi, and Le Song. 2016. Deep Coevolutionary Network: Embedding User and Item Features for Recommendation. arXiv preprint arXiv:1609.03675 (2016). https:\/\/arxiv.org\/pdf\/1609.03675.pdf"},{"key":"e_1_3_2_1_13_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum\u00bfid=B1e3OlStPB","author":"Defferrard Micha\u00ebl","year":"2020","unstructured":"Micha\u00ebl Defferrard, Martino Milani, Fr\u00e9d\u00e9rick Gusset, and Nathana\u00ebl Perraudin. 2020. DeepSphere: a Graph-based Spherical CNN. In International Conference on Learning Representations. https:\/\/openreview.net\/forum\u00bfid=B1e3OlStPB"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482242"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401049"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00454-002-0743-x"},{"key":"e_1_3_2_1_17_1","volume-title":"ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network. In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 111\u2013120","author":"Fu Xingcheng","year":"2021","unstructured":"Xingcheng Fu, Jianxin Li, Jia Wu, Qingyun Sun, Cheng Ji, Senzhang Wang, Jiajun Tan, Hao Peng, and Philip\u00a0S. Yu. 2021. ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network. In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 111\u2013120."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Octavian Ganea Gary B\u00e9cigneul and Thomas Hofmann. 2018. Hyperbolic Neural Networks. In Advances in Neural Information Processing Systems. 5345\u20135355.","DOI":"10.1109\/TNNLS.2018.2798162"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4613-9586-7_3"},{"key":"e_1_3_2_1_20_1","volume-title":"Learning Mixed-Curvature Representations in Product Spaces. In International Conference on Learning Representations. https:\/\/openreview.net\/forum\u00bfid=HJxeWnCcF7","author":"Gu Albert","year":"2019","unstructured":"Albert Gu, Frederic Sala, Beliz Gunel, and Christopher R\u00e9. 2019. Learning Mixed-Curvature Representations in Product Spaces. In International Conference on Learning Representations. https:\/\/openreview.net\/forum\u00bfid=HJxeWnCcF7"},{"key":"e_1_3_2_1_21_1","volume-title":"Hyperbolic Attention Networks. In International Conference on Learning Representations.","author":"Gulcehre Caglar","year":"2019","unstructured":"Caglar Gulcehre, Misha Denil, Mateusz Malinowski, Ali Razavi, Razvan Pascanu, Karl\u00a0Moritz Hermann, Peter Battaglia, Victor Bapst, David Raposo, Adam Santoro, and Nando de Freitas. 2019. Hyperbolic Attention Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i6.20638"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2600428.2609558"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3233186"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2911451.2911531"},{"key":"e_1_3_2_1_26_1","first-page":"1","article-title":"Representation Learning for Dynamic Graphs: A Survey.","volume":"21","author":"Kazemi Seyed\u00a0Mehran","year":"2020","unstructured":"Seyed\u00a0Mehran Kazemi, Rishab Goel, Kshitij Jain, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, and Pascal Poupart. 2020. Representation Learning for Dynamic Graphs: A Survey.Journal of Machine Learning Research 21, 70 (2020), 1\u201373.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450020"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330895"},{"volume-title":"Introduction to Riemannian Manifolds","author":"Lee M","key":"e_1_3_2_1_29_1","unstructured":"John\u00a0M Lee. 2018. Introduction to Riemannian Manifolds. Springer."},{"key":"e_1_3_2_1_30_1","volume-title":"Advances in Neural Information Processing Systems, H.\u00a0Wallach, H.\u00a0Larochelle, A.\u00a0Beygelzimer, F.\u00a0d'Alch\u00e9-Buc, E.\u00a0Fox, and R.\u00a0Garnett (Eds.). Vol.\u00a032. Curran Associates","author":"Liu Qi","year":"2019","unstructured":"Qi Liu, Maximilian Nickel, and Douwe Kiela. 2019. Hyperbolic Graph Neural Networks. In Advances in Neural Information Processing Systems, H.\u00a0Wallach, H.\u00a0Larochelle, A.\u00a0Beygelzimer, F.\u00a0d'Alch\u00e9-Buc, E.\u00a0Fox, and R.\u00a0Garnett (Eds.). Vol.\u00a032. Curran Associates, Inc.https:\/\/proceedings.neurips.cc\/paper\/2019\/file\/103303dd56a731e377d01f6a37badae3-Paper.pdf"},{"key":"e_1_3_2_1_31_1","unstructured":"Emile Mathieu Charline Le\u00a0Lan Chris\u00a0J Maddison Ryota Tomioka and Yee\u00a0Whye Teh. 2019. Continuous Hierarchical Representations with Poincar\u00e9 Variational Auto-Encoders. In Advances in Neural Information Processing Systems. 12544\u201312555."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3412219"},{"key":"e_1_3_2_1_33_1","volume-title":"Continuous-time Dynamic Network Embeddings. In Companion Proceedings of the Web Conference","author":"Nguyen Giang\u00a0Hoang","year":"2018","unstructured":"Giang\u00a0Hoang Nguyen, John\u00a0Boaz Lee, Ryan\u00a0A Rossi, Nesreen\u00a0K Ahmed, Eunyee Koh, and Sungchul Kim. 2018. Continuous-time Dynamic Network Embeddings. In Companion Proceedings of the Web Conference 2018. 969\u2013976."},{"key":"e_1_3_2_1_34_1","volume-title":"Advances in Neural Information Processing Systems, I.\u00a0Guyon, U.\u00a0Von Luxburg, S.\u00a0Bengio, H.\u00a0Wallach, R.\u00a0Fergus, S.\u00a0Vishwanathan, and R.\u00a0Garnett (Eds.). Vol.\u00a030. Curran Associates","author":"Nickel Maximillian","year":"2017","unstructured":"Maximillian Nickel and Douwe Kiela. 2017. Poincar\u00e9 Embeddings for Learning Hierarchical Representations. In Advances in Neural Information Processing Systems, I.\u00a0Guyon, U.\u00a0Von Luxburg, S.\u00a0Bengio, H.\u00a0Wallach, R.\u00a0Fergus, S.\u00a0Vishwanathan, and R.\u00a0Garnett (Eds.). Vol.\u00a030. Curran Associates, Inc.https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/59dfa2df42d9e3d41f5b02bfc32229dd-Paper.pdf"},{"key":"e_1_3_2_1_35_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a080)","author":"Nickel Maximillian","year":"2018","unstructured":"Maximillian Nickel and Douwe Kiela. 2018. Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry. In Proceedings of the 35th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a080), Jennifer Dy and Andreas Krause (Eds.). PMLR, 3779\u20133788. https:\/\/proceedings.mlr.press\/v80\/nickel18a.html"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfa.2008.11.001"},{"key":"e_1_3_2_1_37_1","volume-title":"Normalizing Flows on Tori and Spheres. In International Conference on Machine Learning. PMLR, 8083\u20138092","author":"Rezende Danilo\u00a0Jimenez","year":"2020","unstructured":"Danilo\u00a0Jimenez Rezende, George Papamakarios, S\u00e9bastien Racaniere, Michael Albergo, Gurtej Kanwar, Phiala Shanahan, and Kyle Cranmer. 2020. Normalizing Flows on Tori and Spheres. In International Conference on Machine Learning. PMLR, 8083\u20138092."},{"key":"e_1_3_2_1_38_1","volume-title":"Ollivier-ricci Curvature-based Method to Community Detection in Complex Networks. Scientific reports 9, 1","author":"Sia Jayson","year":"2019","unstructured":"Jayson Sia, Edmond Jonckheere, and Paul Bogdan. 2019. Ollivier-ricci Curvature-based Method to Community Detection in Complex Networks. Scientific reports 9, 1 (2019), 1\u201312."},{"key":"e_1_3_2_1_39_1","volume-title":"Mixed-curvature Variational Autoencoders. In International Conference on Learning Representations. https:\/\/openreview.net\/forum\u00bfid=S1g6xeSKDS","author":"Skopek Ondrej","year":"2020","unstructured":"Ondrej Skopek, Octavian-Eugen Ganea, and Gary B\u00e9cigneul. 2020. Mixed-curvature Variational Autoencoders. In International Conference on Learning Representations. https:\/\/openreview.net\/forum\u00bfid=S1g6xeSKDS"},{"key":"e_1_3_2_1_40_1","volume-title":"Advances in Neural Information Processing Systems, H.\u00a0Larochelle, M.\u00a0Ranzato, R.\u00a0Hadsell, M.F. Balcan, and H.\u00a0Lin (Eds.). Vol.\u00a033. Curran Associates","author":"Sonthalia Rishi","year":"2020","unstructured":"Rishi Sonthalia and Anna Gilbert. 2020. Tree! I am no Tree! I am a low dimensional Hyperbolic Embedding. In Advances in Neural Information Processing Systems, H.\u00a0Larochelle, M.\u00a0Ranzato, R.\u00a0Hadsell, M.F. Balcan, and H.\u00a0Lin (Eds.). Vol.\u00a033. Curran Associates, Inc., 845\u2013856. https:\/\/proceedings.neurips.cc\/paper\/2020\/file\/093f65e080a295f8076b1c5722a46aa2-Paper.pdf"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/2016\/06\/063206"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450101"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25586"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557222"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20333"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16563"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-02396-5"},{"key":"e_1_3_2_1_48_1","unstructured":"Abraham\u00a0Albert Ungar. 2010. Barycentric Calculus in Euclidean and Hyperbolic Geometry: A Comparative Introduction. World Scientific."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371850"},{"key":"e_1_3_2_1_50_1","volume-title":"Advances in Neural Information Processing Systems, M.\u00a0Ranzato, A.\u00a0Beygelzimer, Y.\u00a0Dauphin, P.S. Liang, and J.\u00a0Wortman Vaughan (Eds.). Vol.\u00a034. Curran Associates","author":"Wang Yiwei","year":"2021","unstructured":"Yiwei Wang, Yujun Cai, Yuxuan Liang, Henghui Ding, Changhu Wang, Siddharth Bhatia, and Bryan Hooi. 2021. Adaptive Data Augmentation on Temporal Graphs. In Advances in Neural Information Processing Systems, M.\u00a0Ranzato, A.\u00a0Beygelzimer, Y.\u00a0Dauphin, P.S. Liang, and J.\u00a0Wortman Vaughan (Eds.). Vol.\u00a034. Curran Associates, Inc., 1440\u20131452. https:\/\/proceedings.neurips.cc\/paper\/2021\/file\/0b0b0994d12ad343511adfbfc364256e-Paper.pdf"},{"key":"e_1_3_2_1_51_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum\u00bfid=KYPz4YsCPj","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. In International Conference on Learning Representations. https:\/\/openreview.net\/forum\u00bfid=KYPz4YsCPj"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2316836"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3018661.3018689"},{"key":"e_1_3_2_1_54_1","volume-title":"Inductive Representation Learning on Temporal Graphs. In International Conference on Learning Representations. https:\/\/openreview.net\/forum\u00bfid=rJeW1yHYwH","author":"Xu Da","year":"2020","unstructured":"Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan. 2020. Inductive Representation Learning on Temporal Graphs. In International Conference on Learning Representations. https:\/\/openreview.net\/forum\u00bfid=rJeW1yHYwH"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467422"},{"key":"e_1_3_2_1_56_1","volume-title":"Curvature Graph Network. In International Conference on Learning Representations. https:\/\/openreview.net\/forum\u00bfid=BylEqnVFDB","author":"Ye Ze","year":"2020","unstructured":"Ze Ye, Kin\u00a0Sum Liu, Tengfei Ma, Jie Gao, and Chao Chen. 2020. Curvature Graph Network. In International Conference on Learning Representations. https:\/\/openreview.net\/forum\u00bfid=BylEqnVFDB"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449872"},{"key":"e_1_3_2_1_58_1","first-page":"7548","article-title":"Graph Geometry Interaction Learning","volume":"33","author":"Zhu Shichao","year":"2020","unstructured":"Shichao Zhu, Shirui Pan, Chuan Zhou, Jia Wu, Yanan Cao, and Bin Wang. 2020. Graph Geometry Interaction Learning. Advances in Neural Information Processing Systems 33 (2020), 7548\u20137558.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/504"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220054"}],"event":{"name":"WWW '23: The ACM Web Conference 2023","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"],"location":"Austin TX USA","acronym":"WWW '23"},"container-title":["Proceedings of the ACM Web Conference 2023"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3543507.3583353","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,19]],"date-time":"2024-10-19T08:03:26Z","timestamp":1729325006000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3543507.3583353"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,30]]},"references-count":60,"alternative-id":["10.1145\/3543507.3583353","10.1145\/3543507"],"URL":"https:\/\/doi.org\/10.1145\/3543507.3583353","relation":{},"subject":[],"published":{"date-parts":[[2023,4,30]]},"assertion":[{"value":"2023-04-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}