{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T17:43:55Z","timestamp":1757612635027,"version":"3.44.0"},"reference-count":72,"publisher":"Association for Computing Machinery (ACM)","issue":"10","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:p>\n            Predicting missing facts for temporal knowledge graphs (TKGs) is a fundamental task, called temporal knowledge graph completion (TKGC). One key challenge in this task is the imbalance in data distribution, where facts are unevenly spread across entities and timestamps. This imbalance can lead to poor completion performance for long-tail entities and timestamps, and unstable training due to the introduction of false negative samples. Unfortunately, few previous studies have investigated how to mitigate these effects. Moreover, for the first time, we found that existing methods suffer from model preferences, revealing that entities with specific properties (e.g., recently active) are favored by different models. Such preferences will lead to error accumulation and further exacerbate the effects of imbalanced data distribution. To alleviate the impacts of imbalanced data and model preferences, we introduce\n            <jats:italic toggle=\"yes\">Booster<\/jats:italic>\n            , the first data augmentation strategy for TKGs. The unique requirements here lie in generating new samples that fit the complex semantic and temporal patterns within TKGs, and identifying hard-learning samples specific to models. Therefore, we propose a hierarchical scoring algorithm based on triadic closures within TKGs. By incorporating both global semantic patterns and local time-aware structures, the algorithm enables pattern-aware validation for new samples. Meanwhile, we propose a two-stage training approach to identify samples that deviate from the model's preferred patterns. With a frequency-based filtering strategy, this approach also helps to avoid the misleading of false negatives. Experiments justify that\n            <jats:italic toggle=\"yes\">Booster<\/jats:italic>\n            can seamlessly adapt to existing TKGC models and achieve on average 4.5% performance improvement.\n          <\/jats:p>","DOI":"10.14778\/3748191.3748216","type":"journal-article","created":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T13:50:16Z","timestamp":1756993816000},"page":"3573-3586","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Pattern-Aware Data Augmentation for Temporal Knowledge Graph Completion"],"prefix":"10.14778","volume":"18","author":[{"given":"Jiasheng","family":"Zhang","sequence":"first","affiliation":[{"name":"Xidian University, Xi'an, China"}]},{"given":"Deqiang","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Chongqing University, Chongqing, China"}]},{"given":"Shuang","family":"Liang","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}]},{"given":"Jie","family":"Shao","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}]}],"member":"320","published-online":{"date-parts":[[2025,9,4]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Antoine Bordes Nicolas Usunier Alberto Garc\u00eda-Dur\u00e1n Jason Weston and Oksana Yakhnenko. 2013. Translating Embeddings for Modeling Multi-relational Data. In NIPS. 2787\u20132795."},{"key":"e_1_2_1_2_1","unstructured":"Elizabeth Boschee Jennifer Lautenschlager Sean O'Brien Steve Shellman James Starz and Michael Ward. 2015. ICEWS Coded Event Data. Harvard Dataverse."},{"key":"e_1_2_1_3_1","volume-title":"Knowledge Graph Completion with Counterfactual Augmentation. In The Web Conference. 2611\u20132620","author":"Chang Heng","year":"2023","unstructured":"Heng Chang, Jie Cai, and Jia Li. 2023. Knowledge Graph Completion with Counterfactual Augmentation. In The Web Conference. 2611\u20132620."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2025.3545958"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109951"},{"key":"e_1_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Wei Chen Huaiyu Wan Yuting Wu Shuyuan Zhao Jiayaqi Cheng Yuxin Li and Youfang Lin. 2024. Local-Global History-Aware Contrastive Learning for Temporal Knowledge Graph Reasoning. In ICDE. 733\u2013746.","DOI":"10.1109\/ICDE60146.2024.00062"},{"key":"e_1_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Xiangnan Chen Wen Zhang Zhen Yao Mingyang Chen and Siliang Tang. 2023. Negative Sampling with Adaptive Denoising Mixup for Knowledge Graph Embedding. In ISWC. 253\u2013270.","DOI":"10.1007\/978-3-031-47240-4_14"},{"key":"e_1_2_1_8_1","volume-title":"SGCL: Semantic-aware Graph Contrastive Learning with Lipschitz Graph Augmentation. In ICDE. 3028\u20133041.","author":"Cui Jinhao","year":"2024","unstructured":"Jinhao Cui, Heyan Chai, Xu Yang, Ye Ding, Binxing Fang, and Qing Liao. 2024. SGCL: Semantic-aware Graph Contrastive Learning with Lipschitz Graph Augmentation. In ICDE. 3028\u20133041."},{"key":"e_1_2_1_9_1","volume-title":"Swayambhu Nath Ray, and Partha P. Talukdar","author":"Dasgupta Shib Sankar","year":"2018","unstructured":"Shib Sankar Dasgupta, Swayambhu Nath Ray, and Partha P. Talukdar. 2018. HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding. In EMNLP. 2001\u20132011."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3575637.3575646"},{"key":"e_1_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Wenying Duan Xiaoxi He Zimu Zhou Lothar Thiele and Hong Rao. 2023. Localised Adaptive Spatial-Temporal Graph Neural Network. In KDD. 448\u2013458.","DOI":"10.1145\/3580305.3599418"},{"key":"e_1_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Fredo Erxleben Michael G\u00fcnther Markus Kr\u00f6tzsch Julian Mendez and Denny Vrandecic. 2014. Introducing Wikidata to the Linked Data Web. In ISWC. 50\u201365.","DOI":"10.1007\/978-3-319-11964-9_4"},{"key":"e_1_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Alberto Garc\u00eda-Dur\u00e1n Sebastijan Dumancic and Mathias Niepert. 2018. Learning Sequence Encoders for Temporal Knowledge Graph Completion. In EMNLP. 4816\u20134821.","DOI":"10.18653\/v1\/D18-1516"},{"key":"e_1_2_1_14_1","volume-title":"Dahl","author":"Gilmer Justin","year":"2017","unstructured":"Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. 2017. Neural Message Passing for Quantum Chemistry. In ICML. 1263\u20131272."},{"key":"e_1_2_1_15_1","volume-title":"Marcus A. Brubaker, and Pascal Poupart.","author":"Goel Rishab","year":"2020","unstructured":"Rishab Goel, Seyed Mehran Kazemi, Marcus A. Brubaker, and Pascal Poupart. 2020. Diachronic Embedding for Temporal Knowledge Graph Completion. In AAAI. 3988\u20133995."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3056502"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.14778\/2733004.2733010"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.14778\/3641204.3641217"},{"key":"e_1_2_1_19_1","first-page":"8230","article-title":"G-Mixup: Graph Data Augmentation for Graph Classification","volume":"162","author":"Han Xiaotian","year":"2022","unstructured":"Xiaotian Han, Zhimeng Jiang, Ninghao Liu, and Xia Hu. 2022. G-Mixup: Graph Data Augmentation for Graph Classification. In ICML, Vol. 162. 8230\u20138248.","journal-title":"ICML"},{"key":"e_1_2_1_20_1","unstructured":"Zhen Han Peng Chen Yunpu Ma and Volker Tresp. 2021. Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs. In ICLR."},{"key":"e_1_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Rikui Huang Wei Wei Xiaoye Qu Shengzhe Zhang Dangyang Chen and Yu Cheng. 2024. Confidence is not Timeless: Modeling Temporal Validity for Rule-based Temporal Knowledge Graph Forecasting. In ACL. 10783\u201310794.","DOI":"10.18653\/v1\/2024.acl-long.580"},{"key":"e_1_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Prachi Jain Sushant Rathi Mausam and Soumen Chakrabarti. 2020. Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols. In EMNLP. 3733\u20133747.","DOI":"10.18653\/v1\/2020.emnlp-main.305"},{"key":"e_1_2_1_23_1","unstructured":"Mingxuan Ju Tong Zhao Wenhao Yu Neil Shah and Yanfang Ye. 2023. Graph-Patcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation. In NeurIPS."},{"key":"e_1_2_1_24_1","first-page":"10661","article-title":"Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learning","volume":"162","author":"Kamigaito Hidetaka","year":"2022","unstructured":"Hidetaka Kamigaito and Katsuhiko Hayashi. 2022. Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learning. In ICML, Vol. 162. 10661\u201310675.","journal-title":"ICML"},{"key":"e_1_2_1_25_1","unstructured":"Timoth\u00e9e Lacroix Guillaume Obozinski and Nicolas Usunier. 2020. Tensor Decompositions for Temporal Knowledge Base Completion. In ICLR."},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-020-00624-7"},{"key":"e_1_2_1_27_1","volume-title":"Schrodt","author":"Leetaru Kalev","year":"2013","unstructured":"Kalev Leetaru and Philip A. Schrodt. 2013. Global Database of Events, Language and Tone. In ISA."},{"key":"e_1_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Youru Li Zhenfeng Zhu Xiaobo Guo Linxun Chen Zhouyin Wang Yinmeng Wang Bing Han and Yao Zhao. 2023. Learning Joint Relational Co-evolution in Spatial-Temporal Knowledge Graph for SMEs Supply Chain Prediction. In KDD. 4426\u20134436.","DOI":"10.1145\/3580305.3599855"},{"key":"e_1_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Zixuan Li Xiaolong Jin Wei Li Saiping Guan Jiafeng Guo Huawei Shen Yuanzhuo Wang and Xueqi Cheng. 2021. Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning. In SIGIR. 408\u2013417.","DOI":"10.1145\/3404835.3462963"},{"key":"e_1_2_1_30_1","article-title":"RevGNN: Negative Sampling Enhanced Contrastive Graph Learning for Academic Reviewer Recommendation","volume":"43","author":"Liao Weibin","year":"2025","unstructured":"Weibin Liao, Yifan Zhu, Yanyan Li, Qi Zhang, Zhonghong Ou, and Xuesong Li. 2025. RevGNN: Negative Sampling Enhanced Contrastive Graph Learning for Academic Reviewer Recommendation. ACM Trans. Inf. Syst. 43, 1 (2025), 1:1\u20131:26.","journal-title":"ACM Trans. Inf. Syst."},{"key":"e_1_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Hongrui Liu Binbin Hu Xiao Wang Chuan Shi Zhiqiang Zhang and Jun Zhou. 2022. Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift. In WWW. 1248\u20131258.","DOI":"10.1145\/3485447.3512172"},{"key":"e_1_2_1_32_1","volume-title":"Topological and Temporal Data Augmentation for Temporal Graph Networks. In Temporal Graph Learning Workshop @ NeurIPS","author":"Liu Haoran","year":"2023","unstructured":"Haoran Liu, Jianling Wang, Kaize Ding, and James Caverlee. 2023. Topological and Temporal Data Augmentation for Temporal Graph Networks. In Temporal Graph Learning Workshop @ NeurIPS 2023."},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3393109"},{"key":"e_1_2_1_34_1","first-page":"566","article-title":"Adagrad - An optimizer for stochastic gradient descent","volume":"6","author":"Agnes Lydia A.","year":"2019","unstructured":"A. Agnes Lydia and F. Sagayaraj Francis. 2019. Adagrad - An optimizer for stochastic gradient descent. Int. J. Inf. Comput. Sci. 6, 5 (2019), 566\u2013568.","journal-title":"Int. J. Inf. Comput. Sci."},{"key":"e_1_2_1_35_1","volume-title":"Negative Sampling in Knowledge Graph Representation Learning: A Review. CoRR abs\/2402.19195","author":"Madushanka Tiroshan","year":"2024","unstructured":"Tiroshan Madushanka and Ryutaro Ichise. 2024. Negative Sampling in Knowledge Graph Representation Learning: A Review. CoRR abs\/2402.19195 (2024)."},{"key":"e_1_2_1_36_1","doi-asserted-by":"crossref","unstructured":"Saurav Manchanda. 2023. Metapath-Guided Data-Augmentation For Knowledge Graphs. In CIKM. 4175\u20134179.","DOI":"10.1145\/3583780.3615186"},{"key":"e_1_2_1_37_1","volume-title":"Syed Waqar Jaffry, and Muhammad Kamran Malik","author":"Nasar Zara","year":"2022","unstructured":"Zara Nasar, Syed Waqar Jaffry, and Muhammad Kamran Malik. 2022. Named Entity Recognition and Relation Extraction: State-of-the-Art. ACM Comput. Surv. 54, 1 (2022), 20:1\u201320:39."},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2025.3538706"},{"key":"e_1_2_1_39_1","doi-asserted-by":"crossref","unstructured":"Harry Shomer Wei Jin Wentao Wang and Jiliang Tang. 2023. Toward Degree Bias in Embedding-Based Knowledge Graph Completion. In WWW. 705\u2013715.","DOI":"10.1145\/3543507.3583544"},{"key":"e_1_2_1_40_1","doi-asserted-by":"crossref","unstructured":"Fabian M. Suchanek Gjergji Kasneci and Gerhard Weikum. 2007. Yago: a core of semantic knowledge. In WWW. 697\u2013706.","DOI":"10.1145\/1242572.1242667"},{"key":"e_1_2_1_41_1","unstructured":"Yongduo Sui Qitian Wu Jiancan Wu Qing Cui Longfei Li Jun Zhou Xiang Wang and Xiangnan He. 2023. Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift. In NeurIPS."},{"key":"e_1_2_1_42_1","doi-asserted-by":"crossref","unstructured":"Shiyin Tan Jingyi You and Dongyuan Li. 2022. Temporality- and Frequency-aware Graph Contrastive Learning for Temporal Network. In CIKM. 1878\u20131888.","DOI":"10.1145\/3511808.3557469"},{"key":"e_1_2_1_43_1","doi-asserted-by":"crossref","unstructured":"Jizhi Tang Yansong Feng and Dongyan Zhao. 2019. Learning to Update Knowledge Graphs by Reading News. In EMNLP-IJCNLP. 2632\u20132641.","DOI":"10.18653\/v1\/D19-1265"},{"key":"e_1_2_1_44_1","article-title":"DHyper: A Recurrent Dual Hypergraph Neural Network for Event Prediction in Temporal Knowledge Graphs","volume":"42","author":"Tang Xing","year":"2024","unstructured":"Xing Tang, Ling Chen, Hongyu Shi, and Dandan Lyu. 2024. DHyper: A Recurrent Dual Hypergraph Neural Network for Event Prediction in Temporal Knowledge Graphs. ACM Trans. Inf. Syst. 42, 5 (2024), 129:1\u2013129:23.","journal-title":"ACM Trans. Inf. Syst."},{"key":"e_1_2_1_45_1","doi-asserted-by":"crossref","unstructured":"Zhenwei Tang Shichao Pei Zhao Zhang Yongchun Zhu Fuzhen Zhuang Robert Hoehndorf and Xiangliang Zhang. 2022. Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion. In IJCAI. 2248\u20132254.","DOI":"10.24963\/ijcai.2022\/312"},{"key":"e_1_2_1_46_1","doi-asserted-by":"crossref","unstructured":"Yuxing Tian Aiwen Jiang Qi Huang Jian Guo and Yiyan Qi. 2024. Latent Diffusion-based Data Augmentation for Continuous-Time Dynamic Graph Model. In KDD. 2900\u20132911.","DOI":"10.1145\/3637528.3671863"},{"key":"e_1_2_1_47_1","article-title":"Spatio-temporal Contrastive Learning-enhanced GNNs for Session-based Recommendation","volume":"42","author":"Wan Zhongwei","year":"2024","unstructured":"Zhongwei Wan, Xin Liu, Benyou Wang, Jiezhong Qiu, Boyu Li, Ting Guo, Guangyong Chen, and Yang Wang. 2024. Spatio-temporal Contrastive Learning-enhanced GNNs for Session-based Recommendation. ACM Trans. Inf. Syst. 42, 2 (2024), 58:1\u201358:26.","journal-title":"ACM Trans. Inf. Syst."},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3274230"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2024.3392957"},{"key":"e_1_2_1_50_1","volume-title":"A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects. CoRR abs\/2308.02457","author":"Wang Jiapu","year":"2023","unstructured":"Jiapu Wang, Boyue Wang, Meikang Qiu, Shirui Pan, Bo Xiong, Heng Liu, Linhao Luo, Tengfei Liu, Yongli Hu, Baocai Yin, and Wen Gao. 2023. A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects. CoRR abs\/2308.02457 (2023)."},{"key":"e_1_2_1_51_1","unstructured":"Yiwei Wang Yujun Cai Yuxuan Liang Henghui Ding Changhu Wang Siddharth Bhatia and Bryan Hooi. 2021. Adaptive Data Augmentation on Temporal Graphs. In NeurIPS. 1440\u20131452."},{"key":"e_1_2_1_52_1","volume-title":"Jackie Chi Kit Cheung, and William L. Hamilton","author":"Wu Jiapeng","year":"2020","unstructured":"Jiapeng Wu, Meng Cao, Jackie Chi Kit Cheung, and William L. Hamilton. 2020. TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion. In EMNLP. 5730\u20135746."},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3170559"},{"key":"e_1_2_1_54_1","doi-asserted-by":"crossref","unstructured":"Hao Xin and Lei Chen. 2024. KartGPS: Knowledge Base Update with Temporal Graph Pattern-based Semantic Rules. In ICDE. 5075\u20135087.","DOI":"10.1109\/ICDE60146.2024.00105"},{"key":"e_1_2_1_55_1","volume-title":"TILP: Differentiable Learning of Temporal Logical Rules on Knowledge Graphs. In ICLR.","author":"Xiong Siheng","year":"2023","unstructured":"Siheng Xiong, Yuan Yang, Faramarz Fekri, and James Clayton Kerce. 2023. TILP: Differentiable Learning of Temporal Logical Rules on Knowledge Graphs. In ICLR."},{"key":"e_1_2_1_56_1","unstructured":"Chengjin Xu Yung-Yu Chen Mojtaba Nayyeri and Jens Lehmann. 2021. Temporal Knowledge Graph Completion using a Linear Temporal Regularizer and Multivector Embeddings. In NAACL-HLT. 2569\u20132578."},{"key":"e_1_2_1_57_1","volume-title":"Hamed Shariat Yazdi, and Jens Lehmann","author":"Xu Chenjin","year":"2020","unstructured":"Chenjin Xu, Mojtaba Nayyeri, Fouad Alkhoury, Hamed Shariat Yazdi, and Jens Lehmann. 2020. Temporal Knowledge Graph Completion Based on Time Series Gaussian Embedding. In ISWC. 654\u2013671."},{"key":"e_1_2_1_58_1","volume-title":"Hamed Shariat Yazdi, and Jens Lehmann","author":"Xu Chengjin","year":"2020","unstructured":"Chengjin Xu, Mojtaba Nayyeri, Fouad Alkhoury, Hamed Shariat Yazdi, and Jens Lehmann. 2020. TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation. In COLING. 1583\u20131593."},{"key":"e_1_2_1_59_1","doi-asserted-by":"crossref","unstructured":"Yi Xu Junjie Ou Hui Xu and Luoyi Fu. 2023. Temporal Knowledge Graph Reasoning with Historical Contrastive Learning. In AAAI. 4765\u20134773.","DOI":"10.1609\/aaai.v37i4.25601"},{"key":"e_1_2_1_60_1","volume-title":"Tensor decompositions for temporal knowledge graph completion with time perspective. Expert Syst. Appl. 237, Part A","author":"Yang Jinfa","year":"2024","unstructured":"Jinfa Yang, Xianghua Ying, Yongjie Shi, and Bowei Xing. 2024. Tensor decompositions for temporal knowledge graph completion with time perspective. Expert Syst. Appl. 237, Part A (2024), 121267."},{"key":"e_1_2_1_61_1","volume-title":"SSTKG: Simple Spatio-Temporal Knowledge Graph for Intepretable and Versatile Dynamic Information Embedding. In WWW. 551\u2013559.","author":"Yang Ruiyi","year":"2024","unstructured":"Ruiyi Yang, Flora D. Salim, and Hao Xue. 2024. SSTKG: Simple Spatio-Temporal Knowledge Graph for Intepretable and Versatile Dynamic Information Embedding. In WWW. 551\u2013559."},{"key":"e_1_2_1_62_1","doi-asserted-by":"crossref","unstructured":"Naimeng Yao Qing Liu Yi Yang Weihua Li and Quan Bai. 2023. Entity-Relation Distribution-Aware Negative Sampling for Knowledge Graph Embedding. In ISWC. 234\u2013252.","DOI":"10.1007\/978-3-031-47240-4_13"},{"key":"e_1_2_1_63_1","unstructured":"Yuanzhou Yao Zhao Zhang Yongjun Xu and Chao Li. 2022. Data Augmentation for Few-Shot Knowledge Graph Completion from Hierarchical Perspective. In COLING. 2494\u20132503."},{"key":"e_1_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119853"},{"key":"e_1_2_1_65_1","doi-asserted-by":"crossref","unstructured":"Mengqi Zhang Yuwei Xia Qiang Liu Shu Wu and Liang Wang. 2023. Learning Long- and Short-term Representations for Temporal Knowledge Graph Reasoning. In WWW. 2412\u20132422.","DOI":"10.1145\/3543507.3583242"},{"key":"e_1_2_1_66_1","volume-title":"Jensen","author":"Zhang Qianru","year":"2024","unstructured":"Qianru Zhang, Lianghao Xia, Xuheng Cai, Siu-Ming Yiu, Chao Huang, and Christian S. Jensen. 2024. Graph Augmentation for Recommendation. In ICDE. 557\u2013569."},{"key":"e_1_2_1_67_1","doi-asserted-by":"crossref","unstructured":"Shengzhe Zhang Liyi Chen Chao Wang Shuangli Li and Hui Xiong. 2024. Temporal Graph Contrastive Learning for Sequential Recommendation. In AAAI. 9359\u20139367.","DOI":"10.1609\/aaai.v38i8.28789"},{"volume-title":"TGEditor","author":"Zhang Shuaicheng","key":"e_1_2_1_68_1","unstructured":"Shuaicheng Zhang, Yada Zhu, and Dawei Zhou. 2023. TGEditor: Task-Guided Graph Editing for Augmenting Temporal Financial Transaction Networks. In ICAIF. 219\u2013226."},{"key":"e_1_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-020-00640-7"},{"key":"e_1_2_1_70_1","article-title":"Time-aware Path Reasoning on Knowledge Graph for Recommendation","volume":"41","author":"Zhao Yuyue","year":"2023","unstructured":"Yuyue Zhao, Xiang Wang, Jiawei Chen, Yashen Wang, Wei Tang, Xiangnan He, and Haiyong Xie. 2023. Time-aware Path Reasoning on Knowledge Graph for Recommendation. ACM Trans. Inf. Syst. 41, 2 (2023), 26:1\u201326:26.","journal-title":"ACM Trans. Inf. Syst."},{"key":"e_1_2_1_71_1","doi-asserted-by":"crossref","unstructured":"Xinyi Zhu Liping Wang Hao Xin Xiaohan Wang Zhifeng Jia Jiyao Wang Chunming Ma and Yuxiang Zengt. 2023. T-FinKB: A Platform of Temporal Financial Knowledge Base Construction. In ICDE. 3671\u20133674.","DOI":"10.1109\/ICDE55515.2023.00295"},{"key":"e_1_2_1_72_1","unstructured":"Yanqiao Zhu Yichen Xu Qiang Liu and Shu Wu. 2021. An Empirical Study of Graph Contrastive Learning. In NeurIPS Datasets and Benchmarks."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3748191.3748216","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T13:51:24Z","timestamp":1756993884000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3748191.3748216"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":72,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["10.14778\/3748191.3748216"],"URL":"https:\/\/doi.org\/10.14778\/3748191.3748216","relation":{},"ISSN":["2150-8097"],"issn-type":[{"type":"print","value":"2150-8097"}],"subject":[],"published":{"date-parts":[[2025,6]]},"assertion":[{"value":"2025-09-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}