{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:22:45Z","timestamp":1771024965965,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":45,"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":"Natural Science Foundation of China","award":["62272023, 51991395, 51991391, U1811463"],"award-info":[{"award-number":["62272023, 51991395, 51991391, U1811463"]}]},{"name":"S\\&T Program of Hebei","award":["225A0802D"],"award-info":[{"award-number":["225A0802D"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671770","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:55:12Z","timestamp":1724561712000},"page":"421-432","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Co-Neighbor Encoding Schema: A Light-cost Structure Encoding Method for Dynamic Link Prediction"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6063-6129","authenticated-orcid":false,"given":"Ke","family":"Cheng","sequence":"first","affiliation":[{"name":"CCSE Lab, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9217-967X","authenticated-orcid":false,"given":"Peng","family":"Linzhi","sequence":"additional","affiliation":[{"name":"CCSE Lab, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2677-0751","authenticated-orcid":false,"given":"Junchen","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Transportation Science and Engineering, 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, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6297-5212","authenticated-orcid":false,"given":"Bowen","family":"Du","sequence":"additional","affiliation":[{"name":"Zhongguancun Lab &amp; School of Transportation Science and Engineering, Beihang University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Martin Grohe, and Thomas Lukasiewicz.","author":"Abboud Ralph","year":"2020","unstructured":"Ralph Abboud, 'smail lkan Ceylan, Martin Grohe, and Thomas Lukasiewicz. 2020. The Surprising Power of Graph Neural Networks with Random Node Initialization. arXiv preprint arXiv:2010.01179 (2020). ^5^"},{"key":"e_1_3_2_2_2_1","first-page":"8017","article-title":"Subgraph Neural Networks","volume":"33","author":"Alsentzer Emily","year":"2020","unstructured":"Emily Alsentzer, Samuel Finlayson, Michelle Li, and Marinka Zitnik. 2020. Subgraph Neural Networks. In Advances in Neural Information Processing Systems (NeurIPS), Vol. 33. 8017--8029.","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41562-020-01024-1"},{"key":"e_1_3_2_2_4_1","volume-title":"Improving graph neural network expressivity via subgraph isomorphism counting","author":"Bouritsas Giorgos","year":"2022","unstructured":"Giorgos Bouritsas, Fabrizio Frasca, Stefanos P Zafeiriou, and Michael Bronstein. 2022. Improving graph neural network expressivity via subgraph isomorphism counting. IEEE Transactions on Pattern Analysis and Machine Intelligence (2022)."},{"key":"e_1_3_2_2_5_1","volume-title":"Graph neural networks for link prediction with subgraph sketching. arXiv preprint arXiv:2209.15486","author":"Chamberlain Benjamin Paul","year":"2022","unstructured":"Benjamin Paul Chamberlain, Sergey Shirobokov, Emanuele Rossi, Fabrizio Frasca, Thomas Markovich, Nils Hammerla, Michael M Bronstein, and Max Hansmire. 2022. Graph neural networks for link prediction with subgraph sketching. arXiv preprint arXiv:2209.15486 (2022)."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441806"},{"key":"e_1_3_2_2_7_1","volume-title":"GC-LSTM: Graph convolution embedded LSTM for dynamic network link prediction. Applied Intelligence","author":"Chen Jinyin","year":"2022","unstructured":"Jinyin Chen, XuekeWang, and Xuanheng Xu. 2022. GC-LSTM: Graph convolution embedded LSTM for dynamic network link prediction. Applied Intelligence (2022), 1--16."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2019.2932913"},{"key":"e_1_3_2_2_9_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, HaoWu, 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_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASONAM.2018.8508272"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/288"},{"key":"e_1_3_2_2_12_1","volume-title":"Temporal link prediction using matrix and tensor factorizations. ACMTransactions on Knowledge Discovery from Data (TKDD) 5, 2","author":"Dunlavy Daniel M","year":"2011","unstructured":"Daniel M Dunlavy, Tamara G Kolda, and Evrim Acar. 2011. Temporal link prediction using matrix and tensor factorizations. ACMTransactions on Knowledge Discovery from Data (TKDD) 5, 2 (2011), 1--27."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482242"},{"key":"e_1_3_2_2_14_1","volume-title":"Advances in Neural Information Processing Systems (NeurIPS)","volume":"35","author":"Frasca Fabrizio","year":"2022","unstructured":"Fabrizio Frasca, Beatrice Bevilacqua, Michael M Bronstein, and Haggai Maron. 2022. Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries. In Advances in Neural Information Processing Systems (NeurIPS), Vol. 35."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-24797-2"},{"key":"e_1_3_2_2_16_1","volume-title":"DyRep: Learning Representations Over Dynamic Graphs. arXiv preprint arXiv:1803.04051","author":"Kumar Srijan","year":"2018","unstructured":"Srijan Kumar, Xikun Zhang, and Jure Leskovec. 2018. DyRep: Learning Representations Over Dynamic Graphs. arXiv preprint arXiv:1803.04051 (2018). ^18^"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330895"},{"key":"e_1_3_2_2_18_1","volume-title":"Dynamic Graph Collaborative Filtering. arXiv preprint arXiv:2101.02844","author":"Li Xiaohan","year":"2021","unstructured":"Xiaohan Li, Mengqi Zhang, Shu Wu, Zheng Liu, Liang Wang, and Philip S Yu. 2021. Dynamic Graph Collaborative Filtering. arXiv preprint arXiv:2101.02844 (2021)."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.5555\/1241540.1241551"},{"key":"e_1_3_2_2_20_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_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.websem.2018.12.008"},{"key":"e_1_3_2_2_22_1","volume-title":"Towards Better Evaluation for Dynamic Link Prediction. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track.","author":"Poursafaei Farimah","year":"2022","unstructured":"Farimah Poursafaei, Andy Huang, Kellin Pelrine, and Reihaneh Rabbany. 2022. Towards Better Evaluation for Dynamic Link Prediction. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1002\/wics.1347"},{"key":"e_1_3_2_2_24_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_25_1","volume-title":"Bronstein","author":"Rossi Emanuele","year":"2020","unstructured":"Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, and Michael M. Bronstein. 2020. Temporal Graph Networks for Deep Learning on Dynamic Graphs. CoRR abs\/2006.10637 (2020)."},{"key":"e_1_3_2_2_26_1","volume-title":"Random Features Strengthen Graph Neural Networks. arXiv preprint arXiv:2002.03155","author":"Sato Ryoma","year":"2020","unstructured":"Ryoma Sato, Makoto Yamada, and Hisashi Kashima. 2020. Random Features Strengthen Graph Neural Networks. arXiv preprint arXiv:2002.03155 (2020). ^10^"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290989"},{"key":"e_1_3_2_2_28_1","unstructured":"A. H. Souza D. Mesquita S. Kaski and V. Garg. 2022. Provably expressive temporal graph networks. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_2_29_1","volume-title":"Proceedings of the International Conference on Machine Learning (ICML). 9448--9457","author":"Teru Komal K.","year":"2020","unstructured":"Komal K. Teru, Etienne Denis, and Will Hamilton. 2020. Inductive Relation Prediction by Subgraph Reasoning. In Proceedings of the International Conference on Machine Learning (ICML). 9448--9457. ^41^"},{"key":"e_1_3_2_2_30_1","volume-title":"Hamilton","author":"Teru Komal K.","year":"2022","unstructured":"Komal K. Teru, Etienne Denis, and William L. Hamilton. 2022. Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning. arXiv preprint arXiv:2201.07858v1 [cs.LG] (2022). ^42^"},{"key":"e_1_3_2_2_31_1","volume-title":"international conference on machine learning. PMLR, 3462--3471","author":"Trivedi Rakshit","year":"2017","unstructured":"Rakshit Trivedi, Hanjun Dai, YichenWang, and Le Song. 2017. Know-evolve: Deep temporal reasoning for dynamic knowledge graphs. In international conference on machine learning. PMLR, 3462--3471."},{"key":"e_1_3_2_2_32_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_2_33_1","volume-title":"International Conference on Machine Learning (ICML). 3462--3471","author":"Wang Y.","year":"2017","unstructured":"Wang Y. Song L.. Trivedi R., Dai H. 2017. Know-evolve: Deep temporal reasoning for dynamic knowledge graphs. In International Conference on Machine Learning (ICML). 3462--3471."},{"key":"e_1_3_2_2_34_1","volume-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 3638--3648","author":"Ying Rex","year":"2021","unstructured":"AndrewZWang, Rex Ying, Pan Li, Nikhil Rao, Karthik Subbian, and Jure Leskovec. 2021. Bipartite dynamic representations for abuse detection. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 3638--3648."},{"key":"e_1_3_2_2_35_1","volume-title":"Tcl: Transformer-based dynamic graph modelling via contrastive learning. arXiv preprint arXiv:2105.07944","author":"Chang Xiaofu","year":"2021","unstructured":"LuWang, 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. arXiv preprint arXiv:2105.07944 (2021)."},{"key":"e_1_3_2_2_36_1","volume-title":"International Conference on Learning Representations (ICLR).","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 (ICLR)."},{"key":"e_1_3_2_2_37_1","volume-title":"Inductive Representation Learning on Temporal Graphs. arXiv preprint arXiv:2002.07962","author":"Xu Da","year":"2020","unstructured":"Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan. 2020. Inductive Representation Learning on Temporal Graphs. arXiv preprint arXiv:2002.07962 (2020)."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.09.043"},{"key":"e_1_3_2_2_39_1","volume-title":"andWeifeng Lv","author":"Yu Le","year":"2022","unstructured":"Le Yu, Zihang Liu, Tongyu Zhu, Leilei Sun, Bowen Du, andWeifeng Lv. 2022. Modelling Evolutionary and Stationary User Preferences for Temporal Sets Prediction. arXiv preprint arXiv:2204.05490 (2022)."},{"key":"e_1_3_2_2_40_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_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512064"},{"key":"e_1_3_2_2_42_1","volume-title":"andWeiWang","author":"Yu Wenchao","year":"2017","unstructured":"Wenchao Yu,Wei Cheng, Charu C Aggarwal, Haifeng Chen, andWeiWang. 2017. Link prediction with spatial and temporal consistency in dynamic networks.. In IJCAI. 3343--3349."},{"key":"e_1_3_2_2_43_1","volume-title":"Link Prediction Based on Graph Neural Networks. Advances in Neural Information Processing Systems (NeurIPS)","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 (NeurIPS) (2018). ^23^"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-32597-7_30"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220054"}],"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\/3637528.3671770","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671770","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:13Z","timestamp":1750291453000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671770"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":45,"alternative-id":["10.1145\/3637528.3671770","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671770","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"}}]}}