{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T02:59:38Z","timestamp":1769655578768,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":42,"publisher":"ACM","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62322201,U23B2020,U22A2028,92373110"],"award-info":[{"award-number":["62322201,U23B2020,U22A2028,92373110"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["JKF-202501234364,JKF-20240598"],"award-info":[{"award-number":["JKF-202501234364,JKF-20240598"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State Key Laboratory of Complex & Critical Software Environment","award":["SKLCCSE-2025ZX-04"],"award-info":[{"award-number":["SKLCCSE-2025ZX-04"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,1,28]]},"DOI":"10.1145\/3774934.3786450","type":"proceedings-article","created":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T15:25:57Z","timestamp":1769613957000},"page":"564-576","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["APERTURE: Algorithm-System Co-optimization for Temporal Graph Network Inference"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-8809-824X","authenticated-orcid":false,"given":"Yiqing","family":"Wang","sequence":"first","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1101-7927","authenticated-orcid":false,"given":"Hailong","family":"Yang","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2322-2922","authenticated-orcid":false,"given":"Enze","family":"Yu","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2927-362X","authenticated-orcid":false,"given":"Qingxiao","family":"Sun","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0393-7627","authenticated-orcid":false,"given":"Kejie","family":"Ma","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3261-3483","authenticated-orcid":false,"given":"Kaige","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1399-0352","authenticated-orcid":false,"given":"Chenhao","family":"Xie","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5382-1473","authenticated-orcid":false,"given":"Depei","family":"Qian","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,1,28]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671770"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3676641.3716250"},{"key":"e_1_3_2_1_3_1","volume-title":"Gate-variants of gated recurrent unit (GRU) neural networks. In 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS). 1597\u20131600","author":"Dey Rahul","unstructured":"Rahul Dey and Fathi M Salem. 2017. Gate-variants of gated recurrent unit (GRU) neural networks. In 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS). 1597\u20131600."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i11.33264"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.14778\/3641204.3641215"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3654977"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.4249\/scholarpedia.1888"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.14778\/3641204.3641217"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437801.3441585"},{"key":"e_1_3_2_1_10_1","first-page":"2056","article-title":"Temporal graph benchmark for machine learning on temporal graphs","volume":"36","author":"Huang Shenyang","year":"2023","unstructured":"Shenyang Huang, Farimah Poursafaei, Jacob Danovitch, Matthias Fey, Weihua Hu, Emanuele Rossi, Jure Leskovec, Michael Bronstein, Guillaume Rabusseau, and Reihaneh Rabbany. 2023. Temporal graph benchmark for machine learning on temporal graphs. Advances in Neural Information Processing Systems, 36 (2023), 2056\u20132073.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403142"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi12030100"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.14778\/3705829.3705844"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330895"},{"key":"e_1_3_2_1_15_1","volume-title":"CROSS: Compiler-Driven Optimization of Sparse DNNs Using Sparse\/Dense Computation Kernels. In 2025 IEEE International Symposium on High Performance Computer Architecture (HPCA). 963\u2013976","author":"Liu Fangxin","year":"2025","unstructured":"Fangxin Liu, Shiyuan Huang, Ning Yang, Zongwu Wang, Haomin Li, and Li Jiang. 2025. CROSS: Compiler-Driven Optimization of Sparse DNNs Using Sparse\/Dense Computation Kernels. In 2025 IEEE International Symposium on High Performance Computer Architecture (HPCA). 963\u2013976."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3722219"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3489517.3530503"},{"key":"e_1_3_2_1_18_1","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."},{"key":"e_1_3_2_1_19_1","unstructured":"Oleksandr Shchur and Stephan G\u00fcnnemann. 2019. Overlapping community detection with graph neural networks. arXiv preprint arXiv:1909.12201."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671844"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3710848.3710854"},{"key":"e_1_3_2_1_22_1","volume-title":"International conference on learning representations.","author":"Trivedi Rakshit","year":"2019","unstructured":"Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, and Hongyuan Zha. 2019. Dyrep: Learning representations over dynamic graphs. In International conference on learning representations."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3572848.3577487"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657791"},{"key":"e_1_3_2_1_25_1","unstructured":"Xiaoyun Wang Minhao Cheng Joe Eaton Cho-Jui Hsieh and Felix Wu. 2018. Attack graph convolutional networks by adding fake nodes. arXiv preprint arXiv:1810.10751."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457564"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","unstructured":"Yiqing Wang. 2025. PPoPP26_AE_APERTURE_CODE. https:\/\/doi.org\/10.5281\/zenodo.17710612 10.5281\/zenodo.17710612","DOI":"10.5281\/zenodo.17710612"},{"key":"e_1_3_2_1_28_1","volume-title":"15th USENIX symposium on operating systems design and implementation (OSDI 21)","author":"Wang Yuke","year":"2021","unstructured":"Yuke Wang, Boyuan Feng, Gushu Li, Shuangchen Li, Lei Deng, Yuan Xie, and Yufei Ding. 2021. $GNNAdvisor$: An adaptive and efficient runtime system for $GNN$ acceleration on $GPUs$. In 15th USENIX symposium on operating systems design and implementation (OSDI 21). 515\u2013531."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3572848.3577490"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3620665.3640414"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3494523"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_1_33_1","unstructured":"Da Xu Chuanwei Ruan Evren Korpeoglu Sushant Kumar and Kannan Achan. 2020. Inductive representation learning on temporal graphs. arXiv preprint arXiv:2002.07962."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE60146.2024.00255"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3696410.3714520"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539300"},{"key":"e_1_3_2_1_37_1","volume-title":"Link prediction based on graph neural networks. Advances in neural information processing systems, 31","author":"Zhang Muhan","year":"2018","unstructured":"Muhan Zhang and Yixin Chen. 2018. Link prediction based on graph neural networks. Advances in neural information processing systems, 31 (2018)."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2023.3279302"},{"key":"e_1_3_2_1_39_1","volume-title":"Gnnflow: A distributed framework for continuous temporal gnn learning on dynamic graphs. arXiv preprint arXiv:2311.17410.","author":"Zhong Yuchen","year":"2023","unstructured":"Yuchen Zhong, Guangming Sheng, Tianzuo Qin, Minjie Wang, Quan Gan, and Chuan Wu. 2023. Gnnflow: A distributed framework for continuous temporal gnn learning on dynamic graphs. arXiv preprint arXiv:2311.17410."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"crossref","unstructured":"Hongkuan Zhou Ajitesh Srivastava Hanqing Zeng Rajgopal Kannan and Viktor Prasanna. 2021. Accelerating large scale real-time GNN inference using channel pruning. arXiv preprint arXiv:2105.04528.","DOI":"10.14778\/3461535.3461547"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.14778\/3529337.3529342"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3581784.3607056"}],"event":{"name":"PPoPP '26: 31st ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming","location":"Sydney NSW Australia","acronym":"PPoPP '26","sponsor":["SIGHPC ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing","SIGPLAN ACM Special Interest Group on Programming Languages"]},"container-title":["Proceedings of the 31st ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3774934.3786450","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T15:26:22Z","timestamp":1769613982000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3774934.3786450"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,28]]},"references-count":42,"alternative-id":["10.1145\/3774934.3786450","10.1145\/3774934"],"URL":"https:\/\/doi.org\/10.1145\/3774934.3786450","relation":{},"subject":[],"published":{"date-parts":[[2026,1,28]]},"assertion":[{"value":"2026-01-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}