research-article

DHyper: A Recurrent Dual Hypergraph Neural Network for Event Prediction in Temporal Knowledge Graphs

Authors:

Dandan LyuAuthors Info & Claims

ACM Transactions on Information Systems, Volume 42, Issue 5

Article No.: 129, Pages 1 - 23

https://doi.org/10.1145/3653015

Published: 29 April 2024 Publication History

Abstract

Event prediction is a vital and challenging task in temporal knowledge graphs (TKGs), which have played crucial roles in various applications. Recently, many graph neural networks based approaches are proposed to model the graph structure information in TKGs. However, these approaches only construct graphs based on quadruplets and model the pairwise correlation between entities, which fail to capture the high-order correlations among entities. To this end, we propose DHyper, a recurrent Dual Hypergraph neural network for event prediction in TKGs, which simultaneously models the influences of the high-order correlations among both entities and relations. Specifically, a dual hypergraph learning module is proposed to discover the high-order correlations among entities and among relations in a parameterized way. A dual hypergraph message passing network is introduced to perform the information aggregation and representation fusion on the entity hypergraph and the relation hypergraph. Extensive experiments on six real-world datasets demonstrate that DHyper achieves the state-of-the-art performances, outperforming the best baseline by an average of 13.09%, 4.26%, 17.60%, and 18.03% in MRR, Hits@1, Hits@3, and Hits@10, respectively.

References

[1]

Yuyue Zhao, Xiang Wang, Jiawei Chen, Yashen Wang, Wei Tang, Xiangnan He, and Haiyong Xie. 2022. Time-aware path reasoning on knowledge graph for recommendation. ACM Transactions on Information Systems 41, 2 (2022), 1–26.

Digital Library

[2]

Yakun Li, Lei Hou, and Juanzi Li. 2023. Preference-aware graph attention networks for cross-domain recommendations with collaborative knowledge graph. ACM Transactions on Information Systems 41, 3 (2023), 1–26.

Digital Library

[3]

Ling Chen, Xing Tang, Yuntao Qian, Yansheng Li, and Yongjun Zhang. 2022. DACHA: A dual graph convolution based temporal knowledge graph representation learning method using historical relation. ACM Transactions on Knowledge Discovery from Data 16, 3 (2022), 1–18.

Digital Library

[4]

Shib Sankar Dasgupta, Swayambhu Nath Ray, and Partha Talukdar. 2018. HyTE: Hyperplane-based temporally aware knowledge graph embedding. In EMNLP 2001–2011.

[5]

Yashar Deldjoo, Markus Schedl, Paolo Cremonesi, and Gabriella Pasi. 2020. Recommender systems leveraging multimedia content. ACM Computing Surveys 53, 5 (2020), 1–38.

Digital Library

[6]

Songgaojun Deng, Rangwala Huzefa, and Ning Yue. 2020. Dynamic knowledge graph based multi-event forecasting. In KDD. 1585–1595.

Digital Library

[7]

Jingzhi Fang, Yanyan Shen, Yue Wang, and Lei Chen. 2020. Optimizing DNN computation graph using graph substitutions. In VLDB, 13, 12 (2020), 2734–2746.

Digital Library

[8]

Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, and Yue Gao. 2019. Hypergraph neural networks. In AAAI, 3558–3565.

Digital Library

[9]

Yue Gao, Yifan Feng, Shuyi Ji, and Rongrong Ji. 2022. HGNN⁺: General hypergraph neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 3 (2022), 3181–3199.

[10]

Woojeong Jin, Meng Qu, Xisen Jin, and Xiang Ren. 2020. Recurrent event network: Autoregressive structure inference over temporal knowledge graphs. In EMNLP. 6669–6683.

[11]

Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In ICLR. 1–15.

[12]

Julien Leblay and Melisachew Wudage Chekol. 2018. Deriving validity time in knowledge graph. In WWW. 1771–1776.

[13]

Kalev Leetaru and Philip A. Schrodt. 2013. GDELT: Global data on events, location, and tone, 1979–2012. ISA Annual Convention 2, 4 (2013), 1–49.

[14]

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. 408–417.

Digital Library

[15]

Yujia Li, Shiliang Sun, and Jing Zhao. 2022. TiRGN: Time-guided recurrent graph network with local-global historical patterns for temporal knowledge graph reasoning. In IJCAI. 2152–2158.

[16]

Kangzheng Liu, Feng Zhao, Hongxu Chen, Yicong Li, Guandong Xu, and Hai Jin. 2022. DA-Net: Distributed attention network for temporal knowledge graph reasoning. In CIKM. 1289–1298.

Digital Library

[17]

Minh Tam Pham, Thanh Dat Hoang, Minh Hieu Nguyen, Viet Hung Vu, Thanh Trung Huynh, and Quyet Thang Huynh. 2022. Social multi-role discovering with hypergraph embedding for location-based social networks. In ACIIDS. 675–687.

Digital Library

[18]

Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. 2018. Modeling relational data with graph convolutional networks. In ESWC. 593–607.

Digital Library

[19]

Haohai Sun, Jialun Zhong, Yunpu Ma, Zhen Han, and Kun He. 2021. TimeTraveler: Reinforcement learning for temporal knowledge graph forecasting. In EMNLP. 8306–8319.

[20]

Xing Tang, Ling Chen, Jun Cui, and Baogang Wei. 2019. Knowledge representation learning with entity descriptions, hierarchical types, and textual relations. Information Processing and Management 56, 3 (2019), 809–822.

Digital Library

[21]

Xing Tang and Ling Chen. 2023. GTRL: An entity group-aware temporal knowledge graph representation learning method. IEEE Transactions on Knowledge and Data Engineering 99 (2023), 1--16. DOI:

Digital Library

[22]

Rakshit Trivedi, Hanjun Dai, Yichen Wang, and Le Song. 2017. Know-Evolve: Deep temporal reasoning for dynamic knowledge graphs. In ICML. 3462–3471.

[23]

Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9, 11 (2008), 2579–2605.

[24]

Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, and Partha Talukdar. 2020. Composition-based multi-relational graph convolutional networks. In ICLR, 1–16.

[25]

Jianling Wang, Kaize Ding, Liangjie Hong, Huan Liu, and James Caverlee. 2020. Next-item recommendation with sequential hypergraphs. In SIGIR. 1101–1110.

Digital Library

[26]

Jiapeng Wu, Meng Cao, Jackie Chi Kit Cheung, and William L. Hamilton. 2020. TeMP: Temporal message passing for temporal knowledge graph completion. In EMNLP. 5730–5746.

[27]

Lianghao Xia, Chao Huang, Yong Xu, Jiashu Zhao, Dawei Yin, and Jimmy Huang. 2022. Hypergraph contrastive collaborative filtering. In SIGIR. 70–79.

Digital Library

[28]

Dingqi Yang, Bingqing Qu, Jie Yang, and Philippe Cudré-Mauroux. 2020. LBSN2Vec++: Heterogeneous hypergraph embedding for location-based social networks. IEEE Transactions on Knowledge and Data Engineering 34, 4 (2020), 1843–1855.

[29]

Yuhao Yang, Chao Huang, Lianghao Xia, Yuxuan Liang, Yanwei Yu, and Chenliang Li. 2022. Multi-behavior hypergraph-enhanced transformer for sequential recommendation. In KDD. 2263–2274.

Digital Library

[30]

Jaehyuk Yi and Jinkyoo Park. 2020. Hypergraph convolutional recurrent neural network. In KDD. 3366–-3376.

Digital Library

[31]

Nan Yin, Fuli Feng, Zhigang Luo, Xiang Zhang, Wenjie Wang, Xiao Luo, Chong Chen, and Xian-Sheng Hua. 2022. Dynamic hypergraph convolutional network. In ICDE. 1621–1634.

[32]

Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec. 2018. Hierarchical graph representation learning with differentiable pooling. In NIPS. 4801–4811.

[33]

Denis Degterev, Kristina Badrutdinova, and Anna Stepanova. 2017. Interconnections among the United States, Russia and China: Does Kissinger's American leadership formula apply? International Organizations Research Journal 12, 1 (2017), 81–109.

[34]

Jiasheng Zhang, Shuang Liang, Yongpan Sheng, and Jie Shao. 2022. Temporal knowledge graph representation learning with local and global evolutions. Knowledge-Based Systems 251, 9 (2022), 1–13.

[35]

Jiying Zhang, Yuzhao Chen, Xi Xiao, Runiu Lu, and Shu-Tao Xia. 2022. Learnable hypergraph Laplacian for hypergraph learning. In ICASSP. 4503–4507.

[36]

Zhen Zhang, Jiajun Bu, Martin Ester, Jianfeng Zhang, Zhao Li, Chengwei Yao, Huifen Dai, Zhi Yu, and Can Wang. 2021. Hierarchical multi-view graph pooling with structure learning. IEEE Transactions on Knowledge and Data Engineering 35, 1 (2021), 545–559.

[37]

Andre Martins and Ramon Astudillo. 2016. From softmax to sparsemax: A sparse model of attention and multi-label classification. In ICML. 1614–1623.

[38]

James Bergstra, Brent Komer, Chris Eliasmith, Dan Yamins, and David D. Cox. 2015. Hyperopt: A python library for model selection and hyperparameter optimization. Computational Science and Discovery 8, 1 (2015), 1–25.

[39]

Ling Chen, Jun Cui, Xing Tang, Yuntao Qian, Yansheng Li, and Yongjun Zhang. 2022. RLPath: A knowledge graph link prediction method using reinforcement learning based attentive relation path searching and representation learning. Applied Intelligence 52, 4 (2022), 4715–4726.

Digital Library

[40]

Hanrui Wu, Jinyi Long, Nuosi Li, Dahai Yu, and Michael K. Ng. 2022. Adversarial auto-encoder domain adaptation for cold-start recommendation with positive and negative hypergraphs. ACM Transactions on Information Systems 41, 2 (2022), 1–25.

Digital Library

[41]

Chao Shang, Yun Tang, Jing Huang, Jinbo Bi, Xiaodong He, and Bowen Zhou. 2019. End-to-end structure-aware convolutional networks for knowledge base completion. In AAAI. 3060–3067.

Digital Library

[42]

Lei Guo, Hongzhi Yin, Tong Chen, Xiangliang Zhang, and Kai Zheng. 2021. Hierarchical hyperedge embedding-based representation learning for group recommendation. ACM Transactions on Information Systems 40, 1 (2021), 1–27.

Digital Library

[43]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In NIPS. 5998–6008.

Digital Library

[44]

John Boaz Lee, Ryan A. Rossi, Sungchul Kim, Nesreen K. Ahmed, and Eunyee Koh. 2019. Attention models in graphs: A survey. ACM Transactions on Knowledge Discovery from Data 13, 6 (2019), 1–25.

Digital Library

[45]

Yue Zhao, Gao Cong, Jiachen Shi, and Chunyan Miao. 2022. QueryFormer: A tree transformer model for query plan representation. In VLDB 15, 8 (2022), 1658–1670.

Digital Library

[46]

Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In NIPS. 2787–2795.

Digital Library

[47]

Elizabeth Boschee, Jennifer Lautenschlager, Sean O'Brien, Steve Shellman, James Starz, and Michael Ward. 2015. ICEWS Coded Event Data. Harvard Dataverse. DOI:

[48]

Martin Atzmueller. 2019. Onto model-based anomalous link pattern mining on feature-rich social interaction networks. In Companion Proceedings of the 27th World Wide Web Conference. 1047–1050.

Digital Library

[49]

Daniel M. Dunlavy, Tamara G. Kolda, and Evrim Acar. 2011. Temporal link prediction using matrix and tensor factorizations. ACM Transactions on Knowledge Discovery from Data 5, 2 (2011), 1–27.

Digital Library

Cited By

Tang HWu SSun XZeng JXu GLi Q(2024)TCGC: Temporal Collaboration-Aware Graph Co-Evolution Learning for Dynamic RecommendationACM Transactions on Information Systems10.1145/368747043:1(1-27)Online publication date: 27-Aug-2024
https://dl.acm.org/doi/10.1145/3687470
Bhuyan BSingh TTomar RMeraihi YRamdane-Cherif A(2024)A Monadic Second-Order Temporal Logic framework for hypergraphsNeural Computing and Applications10.1007/s00521-024-10365-136:35(22081-22118)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s00521-024-10365-1

Index Terms

DHyper: A Recurrent Dual Hypergraph Neural Network for Event Prediction in Temporal Knowledge Graphs
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 42, Issue 5

September 2024

809 pages

EISSN:1558-2868

DOI:10.1145/3618083

Editor:
Min Zhang
Tsinghua University, China

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Association for Computing Machinery

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Publication History

Published: 29 April 2024

Online AM: 18 March 2024

Accepted: 06 March 2024

Revised: 02 February 2024

Received: 27 April 2023

Published in TOIS Volume 42, Issue 5

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National Key Research and Development Program of China
Donghai Laboratory

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Cited By

Tang HWu SSun XZeng JXu GLi Q(2024)TCGC: Temporal Collaboration-Aware Graph Co-Evolution Learning for Dynamic RecommendationACM Transactions on Information Systems10.1145/368747043:1(1-27)Online publication date: 27-Aug-2024
https://dl.acm.org/doi/10.1145/3687470
Bhuyan BSingh TTomar RMeraihi YRamdane-Cherif A(2024)A Monadic Second-Order Temporal Logic framework for hypergraphsNeural Computing and Applications10.1007/s00521-024-10365-136:35(22081-22118)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s00521-024-10365-1

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