{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T17:01:31Z","timestamp":1778259691135,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":70,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T00:00:00Z","timestamp":1635206400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,10,26]]},"DOI":"10.1145\/3459637.3482014","type":"proceedings-article","created":{"date-parts":[[2021,10,30]],"date-time":"2021-10-30T18:34:11Z","timestamp":1635618851000},"page":"4564-4573","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":133,"title":["PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models"],"prefix":"10.1145","author":[{"given":"Benedek","family":"Rozemberczki","sequence":"first","affiliation":[{"name":"AstraZeneca, Cambridge, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul","family":"Scherer","sequence":"additional","affiliation":[{"name":"University of Cambridge, Cambridge, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yixuan","family":"He","sequence":"additional","affiliation":[{"name":"University of Oxford, Oxford, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George","family":"Panagopoulos","sequence":"additional","affiliation":[{"name":"\u00c9cole Polytechnique, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander","family":"Riedel","sequence":"additional","affiliation":[{"name":"Ernst-Abbe University for Applied Sciences, Jena, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria","family":"Astefanoaei","sequence":"additional","affiliation":[{"name":"IT University of Copenhagen Denmark, Coppenhagen, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oliver","family":"Kiss","sequence":"additional","affiliation":[{"name":"Central European University, Budapest, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ferenc","family":"Beres","sequence":"additional","affiliation":[{"name":"ELKH SZTAKI, Budapest, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guzm\u00e1n","family":"L\u00f3pez","sequence":"additional","affiliation":[{"name":"Tryolabs, Montevideo, Uruguay"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicolas","family":"Collignon","sequence":"additional","affiliation":[{"name":"Pedal Me, London, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rik","family":"Sarkar","sequence":"additional","affiliation":[{"name":"University of Edinburgh, Edinburgh, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,10,30]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/3026877.3026899"},{"key":"e_1_3_2_1_2_1","unstructured":"Sean Anderson Eric Ward Lewis Barnett and Philippina English. 2018. sdmTMB: Spatial and spatiotemporal GLMMs with TMB. https:\/\/github.com\/pbs-assess\/sdmTMB. Sean Anderson Eric Ward Lewis Barnett and Philippina English. 2018. sdmTMB: Spatial and spatiotemporal GLMMs with TMB. https:\/\/github.com\/pbs-assess\/sdmTMB."},{"key":"e_1_3_2_1_3_1","volume-title":"3rd International Conference on Learning Representations, ICLR","author":"Bahdanau Dzmitry","year":"2015"},{"key":"e_1_3_2_1_4_1","volume-title":"Advances in Neural Information Processing Systems","volume":"33","author":"Bai Lei","year":"2020"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41109-019-0169-5"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41109-018-0080-5"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403296"},{"key":"e_1_3_2_1_8_1","volume-title":"URL http:\/\/sphinx-doc.org\/sphinx. pdf","author":"Brandl Georg","year":"2010"},{"key":"e_1_3_2_1_9_1","unstructured":"Yukuo Cen Zhenyu Hou Yan Wang Qibin Chen Yizhen Luo Xingcheng Yao Aohan Zeng Shiguang Guo Peng Zhang Guohao Dai etal 2021. CogDL: An Extensive Toolkit for Deep Learning on Graphs. arXiv preprint arXiv:2103.00959 (2021). Yukuo Cen Zhenyu Hou Yan Wang Qibin Chen Yizhen Luo Xingcheng Yao Aohan Zeng Shiguang Guo Peng Zhang Guohao Dai et al. 2021. CogDL: An Extensive Toolkit for Deep Learning on Graphs. arXiv preprint arXiv:2103.00959 (2021)."},{"key":"e_1_3_2_1_10_1","volume-title":"GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Prediction. arXiv preprint arXiv:1812.04206","author":"Chen Jinyin","year":"2018"},{"key":"e_1_3_2_1_11_1","volume-title":"MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. arXiv preprint arXiv:1512.01274","author":"Chen Tianqi","year":"2015"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"key":"e_1_3_2_1_13_1","unstructured":"CSIRO's Data61. 2018. StellarGraph Machine Learning Library. https:\/\/github.com\/stellargraph\/stellargraph. CSIRO's Data61. 2018. StellarGraph Machine Learning Library. https:\/\/github.com\/stellargraph\/stellargraph."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.5555\/3157382.3157527"},{"key":"e_1_3_2_1_15_1","volume-title":"Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds.","author":"Fey Matthias"},{"key":"e_1_3_2_1_16_1","volume-title":"Matthew James Johnson, and Chris Leary","author":"Frostig Roy","year":"2018"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.5555\/3305381.3305512"},{"key":"e_1_3_2_1_18_1","volume-title":"Jraph: A Library for Graph Neural Networks in Jax","author":"Godwin Jonathan","year":"2020"},{"key":"e_1_3_2_1_19_1","volume-title":"Ninareh Mehrabi, Emilio Ferrara, and Arquimedes Canedo.","author":"Goyal Palash","year":"2018"},{"key":"e_1_3_2_1_20_1","volume-title":"GEM: A Python Package for Graph Embedding Methods. Journal of Open Source Software","author":"Goyal Palash"},{"key":"e_1_3_2_1_21_1","volume-title":"Graph Neural Networks in TensorFlow and Keras with Spektral. arXiv preprint arXiv:2006.12138","author":"Grattarola Daniele","year":"2020"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"e_1_3_2_1_23_1","volume-title":"AGU Fall Meeting Abstracts","volume":"2019","author":"Hanson Matthew","year":"2019"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1140\/epjb\/e2015-60657-4"},{"key":"e_1_3_2_1_26_1","volume-title":"Temporal Networks. Physics reports","author":"Holme Petter","year":"2012"},{"key":"e_1_3_2_1_27_1","volume-title":"Efficient Graph Deep Learning in TensorFlow with TF Geometric. arXiv preprint arXiv:2101.11552","author":"Hu Jun","year":"2021"},{"key":"e_1_3_2_1_28_1","volume-title":"JAX: Composable Transformations of Python+NumPy Programs","author":"Bradbury James","year":"2018"},{"key":"e_1_3_2_1_29_1","volume-title":"Proceedings of the 3rd International Conference on Learning Representations.","author":"Kingma Diederik","year":"2015"},{"key":"e_1_3_2_1_30_1","volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations (ICLR).","author":"Thomas"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330847"},{"key":"e_1_3_2_1_32_1","volume-title":"Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In International Conference on Learning Representations.","author":"Li Yaguang","year":"2018"},{"key":"e_1_3_2_1_33_1","volume-title":"Gated Graph Sequence Neural Networks. In International Conference on Learning Representations (ICLR).","author":"Li Yujia","year":"2016"},{"key":"e_1_3_2_1_34_1","volume-title":"DIG: A Turnkey Library for Diving into Graph Deep Learning Research. arXiv preprint arXiv:2103.12608","author":"Liu Meng","year":"2021"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1166"},{"key":"e_1_3_2_1_36_1","volume-title":"Methods-Time Measurement","author":"Maynard Harold B"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.5555\/3104322.3104425"},{"key":"e_1_3_2_1_38_1","volume-title":"Proceedings of the 35th AAAI Conference on Artificial Intelligence.","author":"Panagopoulos George","year":"2021"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"crossref","unstructured":"Aldo Pareja Giacomo Domeniconi Jie Chen Tengfei Ma Toyotaro Suzumura Hiroki Kanezashi Tim Kaler Tao B Schardl and Charles E Leiserson. 2020. EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs. In AAAI. 5363--5370. Aldo Pareja Giacomo Domeniconi Jie Chen Tengfei Ma Toyotaro Suzumura Hiroki Kanezashi Tim Kaler Tao B Schardl and Charles E Leiserson. 2020. EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs. In AAAI. 5363--5370.","DOI":"10.1609\/aaai.v34i04.5984"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3455008"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3455008"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"crossref","unstructured":"Edzer Pebesma. 2017. staRs: Spatiotemporal Arrays: Raster and Vector Datacubes. https:\/\/github.com\/r-spatial\/stars. Edzer Pebesma. 2017. staRs: Spatiotemporal Arrays: Raster and Vector Datacubes. https:\/\/github.com\/r-spatial\/stars.","DOI":"10.32614\/CRAN.package.stars"},{"key":"e_1_3_2_1_43_1","volume-title":"Handbook of Applied Spatial Analysis","author":"Rey Sergio J"},{"key":"e_1_3_2_1_44_1","unstructured":"Emanuele Rob. 2020. PySTAC: Python library for working with any SpatioTemporal Asset Catalog (STAC). https:\/\/github.com\/stac-utils\/pystac. GitHub repository. Emanuele Rob. 2020. PySTAC: Python library for working with any SpatioTemporal Asset Catalog (STAC). https:\/\/github.com\/stac-utils\/pystac. GitHub repository."},{"key":"e_1_3_2_1_45_1","volume-title":"2020 a. Pathfinder Discovery Networks for Neural Message Passing. arXiv preprint arXiv:2010.12878","author":"Rozemberczki Benedek","year":"2020"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412757"},{"key":"e_1_3_2_1_47_1","unstructured":"Benedek Rozemberczki Paul Scherer Oliver Kiss Rik Sarkar and Tamas Ferenci. 2021. Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks .arxiv: 2102.08100 [cs.LG] Benedek Rozemberczki Paul Scherer Oliver Kiss Rik Sarkar and Tamas Ferenci. 2021. Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks .arxiv: 2102.08100 [cs.LG]"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.5555\/1891996"},{"key":"e_1_3_2_1_49_1","volume-title":"ICML Workshop on Graph Representation Learning and Beyond.","author":"Scherer Paul","year":"2020"},{"key":"e_1_3_2_1_50_1","volume-title":"Modeling Relational Data with Graph Convolutional Networks. In European Semantic Web Conference. Springer, 593--607","author":"Schlichtkrull Michael","year":"2018"},{"key":"e_1_3_2_1_51_1","volume-title":"Structured Sequence Modeling with Graph Convolutional Recurrent Networks. In International Conference on Neural Information Processing. Springer, 362--373","author":"Seo Youngjoo","year":"2018"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01230"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2670313"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3341161.3342872"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308560.3316581"},{"key":"e_1_3_2_1_56_1","unstructured":"Paul Taylor Christopher Harris Thompson Comer and Mark Harris. 2019. CUDA-Accelerated GIS and Spatiotemporal Algorithms. https:\/\/github.com\/rapidsai\/cuspatial. Paul Taylor Christopher Harris Thompson Comer and Mark Harris. 2019. CUDA-Accelerated GIS and Spatiotemporal Algorithms. https:\/\/github.com\/rapidsai\/cuspatial."},{"key":"e_1_3_2_1_57_1","unstructured":"Cunchao Tu Yuan Yao Zhengyan Zhang Ganqu Cui Hao Wang Changxin Tian Jie Zhou and Cheng Yang. 2018. OpenNE: An Open Source Toolkit for Network Embedding. https:\/\/github.com\/thunlp\/OpenNE. Cunchao Tu Yuan Yao Zhengyan Zhang Ganqu Cui Hao Wang Changxin Tian Jie Zhou and Cheng Yang. 2018. OpenNE: An Open Source Toolkit for Network Embedding. https:\/\/github.com\/thunlp\/OpenNE."},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCSE.2011.37"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295349"},{"key":"e_1_3_2_1_60_1","volume-title":"GeoWave: Utilizing Distributed Key-Value Stores for Multidimensional Data. In International Symposium on Spatial and Temporal Databases. Springer, 105--122","author":"Whitby Michael A","year":"2017"},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403118"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3340404"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.5555\/3304222.3304273"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403152"},{"key":"e_1_3_2_1_65_1","volume-title":"MediaPipe Hands: On-device Real-time Hand Tracking .arxiv","author":"Zhang Fan","year":"2006"},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2935152"},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5477"},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366424.3383111"},{"key":"e_1_3_2_1_69_1","volume-title":"A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting. arXiv preprint arXiv:2006.11583","author":"Zhu Jiawei","year":"2020"},{"key":"e_1_3_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1145\/3406534"}],"event":{"name":"CIKM '21: The 30th ACM International Conference on Information and Knowledge Management","location":"Virtual Event Queensland Australia","acronym":"CIKM '21","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 30th ACM International Conference on Information & Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3459637.3482014","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3459637.3482014","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:30:11Z","timestamp":1750188611000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3459637.3482014"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,26]]},"references-count":70,"alternative-id":["10.1145\/3459637.3482014","10.1145\/3459637"],"URL":"https:\/\/doi.org\/10.1145\/3459637.3482014","relation":{},"subject":[],"published":{"date-parts":[[2021,10,26]]},"assertion":[{"value":"2021-10-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}