Author Identifier (ORCID)
Jianxin Li: https://orcid.org/0000-0002-9059-330X
Abstract
Temporal Knowledge Graph (TKG) reasoning seeks to predict future events by analyzing historical data, where the effective leverage of both local and global historical facts proves crucial. Existing approaches employ graph neural networks (GNNs) and recurrent neural networks (RNNs) for local evolution patterns, complemented by statistical methods to enhance attention to global facts, demonstrating efficient predictive capabilities. However, traditional GNNs, constrained by their low-order neighborhood aggregation design, inherently fail to model potential high-order dependencies among facts. Furthermore, existing global history modeling approaches may introduce irrelevant historical information that interferes with prediction tasks. To address these limitations, we propose a Dual History-aware HyperGraph Network for TKG reasoning, namely DHHGN. Specifically, for local history modeling, we design a hybrid hypergraph-graph joint recurrent convolution module that simultaneously captures low-order neighborhood information and high-order interaction patterns among entities, employing a gating mechanism to adaptively blend their contributions. For global history modeling, we propose a dual history enhancement module that amplifies attention on pivotal historical facts while ensuring holistic integration of all historical contexts. Extensive experiments on four public benchmarks validate that DualHist-HGN consistently outperforms existing state-of-the-art methods across TKG reasoning tasks.
Keywords
History-aware, hypergraph, temporal knowledge graph reasoning
Document Type
Conference Proceeding
Date of Publication
4-12-2026
Publication Title
WWW 2026 Proceedings of the ACM Web Conference 2026
Publisher
Association for Computing Machinery
School
School of Business and Law
RAS ID
94355
Funding Information
This work was supported in part by the National Natural Science Foundation of China under Grant 62476247, 62073295 and 62072409, "Pioneer" and "Leading Goose" R&D Program of Zhejiang under Grant 2024C01214, and in part by the Zhejiang Provincial Natural Science Foundation under Grant LR21F020003.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
First Page
3800
Last Page
3810
Comments
Ye, K., Kong, X., Zhang, Y., Wang, X., Zhu, L., Du, J., Shen, G., & Li, J. (2026). Dual history enhancement with hybrid hypergraph-graph networks for temporal knowledge graph reasoning. In Proceedings of the ACM Web Conference 2026 (WWW ’26) (pp. 3800–3810). Association for Computing Machinery. https://doi.org/10.1145/3774904.3792310