{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T15:21:18Z","timestamp":1783610478511,"version":"3.55.0"},"reference-count":63,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61906039"],"award-info":[{"award-number":["61906039"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276110"],"award-info":[{"award-number":["62276110"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972111"],"award-info":[{"award-number":["61972111"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2023,9,1]]},"DOI":"10.1109\/tkde.2022.3209067","type":"journal-article","created":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T20:44:59Z","timestamp":1664225099000},"page":"8784-8798","source":"Crossref","is-referenced-by-count":26,"title":["H-Diffu: Hyperbolic Representations for Information Diffusion Prediction"],"prefix":"10.1109","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6161-9232","authenticated-orcid":false,"given":"Shanshan","family":"Feng","sequence":"first","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen, GuangDong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0984-1629","authenticated-orcid":false,"given":"Kaiqi","family":"Zhao","sequence":"additional","affiliation":[{"name":"University of Auckland, Auckland, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1374-395X","authenticated-orcid":false,"given":"Lanting","family":"Fang","sequence":"additional","affiliation":[{"name":"Southeast University, Jiangsu, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaiyu","family":"Feng","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4488-0102","authenticated-orcid":false,"given":"Wei","family":"Wei","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, Hubei, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1894-984X","authenticated-orcid":false,"given":"Xutao","family":"Li","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen, GuangDong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8264-6117","authenticated-orcid":false,"given":"Ling","family":"Shao","sequence":"additional","affiliation":[{"name":"Terminus Group, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2995075"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2013.2254619"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11856"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080784"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00134"},{"key":"ref59","first-page":"5998","article-title":"Attention is all you need","author":"vaswani","year":"2017","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2017.57"},{"key":"ref58","article-title":"Efficient estimation of word representations in vector space","author":"mikolov","year":"2013","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1103\/RevModPhys.74.47"},{"key":"ref52","article-title":"Hyperbolic deep neural networks: A survey","author":"peng","year":"2021"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3314106"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/2503792.2503797"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/2835776.2835817"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835934"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2939796"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3269275"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/531"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/560"},{"key":"ref51","article-title":"Learning hyperbolic representations of topological features","author":"kyriakis","year":"2021","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00645"},{"key":"ref46","first-page":"7007","article-title":"Graph geometry interaction learning","author":"zhu","year":"2020","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref45","first-page":"4869","article-title":"Hyperbolic graph convolutional neural networks","author":"chami","year":"2019","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371850"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449872"},{"key":"ref42","article-title":"Hyperbolic attention networks","author":"gulcehre","year":"2019","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref41","first-page":"5350","article-title":"Hyperbolic neural networks","author":"ganea","year":"2018","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref44","first-page":"8228","article-title":"Hyperbolic graph neural networks","author":"liu","year":"2019","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.617"},{"key":"ref49","article-title":"Poincaré glove: Hyperbolic word embeddings","author":"tifrea","year":"2019","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/416"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/1718487.1718518"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/2556195.2556216"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2018.00089"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331214"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-85567-5_9"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371811"},{"key":"ref40","first-page":"6066","article-title":"Hyperbolic disk embeddings for directed acyclic graphs","author":"suzuki","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220077"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v12i1.15044"},{"key":"ref37","first-page":"4460","article-title":"Representation tradeoffs for hyperbolic embeddings","author":"de sa","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref36","first-page":"347","article-title":"DyHGCN: A dynamic heterogeneous graph convolutional network to learn users’ dynamic preferences for information diffusion prediction","author":"yuan","year":"2020","journal-title":"Proc Eur Conf Mach Learn Knowl Discovery Databases"},{"key":"ref31","first-page":"925","article-title":"Can cascades be predicted?","year":"2014","journal-title":"Proc Int World Wide Web Conf"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835933"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052643"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783401"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3301303"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/956755.956769"},{"key":"ref39","first-page":"1646","article-title":"Hyperbolic entailment cones for learning hierarchical embeddings","author":"ganea","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref38","first-page":"3672","article-title":"Lorentzian distance learning for hyperbolic representations","author":"law","year":"2019","journal-title":"in Proc Int Conf Mach Learn"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.92.032812"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2013.77"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.82.036106"},{"key":"ref25","first-page":"3776","article-title":"Learning continuous hierarchies in the lorentz model of hyperbolic geometry","author":"nickel","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3184558.3186931"},{"key":"ref63","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2015","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref22","first-page":"6341","article-title":"Poincaré embeddings for learning hierarchical representations","author":"nickel","year":"2017","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.67.026112"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/3433000"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.2200\/S00527ED1V01Y201308DTM037"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2012.100"},{"key":"ref60","first-page":"6393","article-title":"Differentiating through the fréchet mean","author":"lou","year":"2020","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339641"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1038\/srep04343"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/69\/10210449\/09901507.pdf?arnumber=9901507","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T18:10:39Z","timestamp":1693246239000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9901507\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,1]]},"references-count":63,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2022.3209067","relation":{},"ISSN":["1041-4347","1558-2191","2326-3865"],"issn-type":[{"value":"1041-4347","type":"print"},{"value":"1558-2191","type":"electronic"},{"value":"2326-3865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,1]]}}}