Hyperbolic Heterogeneous Hypergraph Neural Networks

JIN Hong; ZHOU Kaicheng; HUANG Xuqin; YOU Lan; CUI Haibo

doi:10.11999/JEIT260207

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JIN Hong, ZHOU Kaicheng, HUANG Xuqin, YOU Lan, CUI Haibo. Hyperbolic Heterogeneous Hypergraph Neural Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260207

Citation:

JIN Hong, ZHOU Kaicheng, HUANG Xuqin, YOU Lan, CUI Haibo. Hyperbolic Heterogeneous Hypergraph Neural Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260207

JIN Hong, ZHOU Kaicheng, HUANG Xuqin, YOU Lan, CUI Haibo. Hyperbolic Heterogeneous Hypergraph Neural Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260207

Citation:

JIN Hong, ZHOU Kaicheng, HUANG Xuqin, YOU Lan, CUI Haibo. Hyperbolic Heterogeneous Hypergraph Neural Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260207

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Hyperbolic Heterogeneous Hypergraph Neural Networks

doi: 10.11999/JEIT260207 cstr: 32379.14.JEIT260207

JIN Hong^{1, 2
,},
ZHOU Kaicheng^1
,,
HUANG Xuqin^1
,,
YOU Lan^{1, 3
,
,},
CUI Haibo^{1, 3
,}

1.
School of Computer Science, Hubei University, Wuhan 430062, China
2.
Hubei Key Laboratory of Big Data Intelligent Analysis and Application (Hubei University), Wuhan 430062, China
3.
Key Laboratory of Intelligent Sensing System and Security (Hubei University), Ministry of Education, Wuhan 430062, China

Funds: Major Science and Technology Project of Hubei Province, Technology Innovation Special Program of Hubei Province, Hubei Province Natural Science Foundation Joint Fund Project

Received Date: 2026-02-27
Accepted Date: 2026-11-02
Rev Recd Date: 2026-11-02

Available Online: 2026-11-09

Abstract

Abstract

Objective Heterogeneous graphs are characterized by diverse node types and multi-semantic relations, the structural complexity of which presents significant challenges for representation learning. Prevailing international research primarily utilizes meta-paths to characterize semantic relations, learning node representations from both single-semantic and multi-semantic perspectives. However, meta-paths are limited to capturing pairwise relationships, making it difficult to model the latent high-order interaction structures within heterogeneous graphs. Furthermore, most existing heterogeneous graph neural networks are built upon Euclidean embeddings; yet, Euclidean space struggles to capture the pervasive hierarchical nature and exponential expansion of heterogeneous graphs within finite dimensions. This leads to the attenuation of hierarchical semantics during the embedding process, thereby compromising representation quality. This research proposes a novel modeling scheme for heterogeneous graph representation learning that integrates high-order structures with non-Euclidean geometric properties, providing robust theoretical support for semantic mining and knowledge discovery in complex information networks. Methods The proposed algorithm achieves a deep representation of high-order semantics and hierarchical structures in heterogeneous graphs by establishing a hyperbolic-Euclidean alternating transformation architecture. The model first constructs a heterogeneous hypergraph based on task-relevant meta-paths, explicitly capturing high-order semantics within the original graph through the capacity of hyperedges to associate multiple nodes. Subsequently, leveraging the geometric property that the tangent space of hyperbolic space is homeomorphic to Euclidean space, the model facilitates efficient feature learning via the alternating application of exponential and logarithmic mappings, thereby preserving the intrinsic hyperbolic geometric characteristics. Finally, a hyperbolic hyperedge attention mechanism is introduced to adaptively fuse semantic features across multiple views within a non-Euclidean manifold, generating the final node embedding representations. Extensive experimental results validate the significant advantages of this approach in modeling hierarchical relationships through negative curvature structures and in capturing high-order semantic information. Results and Discussions Experimental results demonstrate that the proposed HHexGNN model consistently outperforms baseline models across various downstream tasks over multiple standard datasets. By modeling in hyperbolic space, the framework leverages non-Euclidean geometric properties to achieve more pronounced performance gains compared to Euclidean-based models as node dimensions increase, effectively capturing the intricate hierarchical structures of high-dimensional data. For example, on the IMDB dataset, HHexGNN achieves average improvements of 9.88% in Macro-F1 and 10.29% in Micro-F1 relative to HGCN and HHGNN, validating the inherent advantages of semantic information within the hyperbolic hypergraph framework for capturing hierarchical relations. Furthermore, HHexGNN exhibits superior performance in node clustering tasks. For instance, on the DBLP dataset, with NMI and ARI reaching 80.51% and 80.72%, respectively—representing absolute improvements of 4.33% and 3.40% over HGCN, which only models standard graph structures. This gap reveals that capturing high-order interactions through hypergraphs enables the effective encoding of multivariate interaction semantics that are difficult to represent via simple hyperbolic projections, thereby significantly optimizing the representation quality of heterogeneous academic networks. Conclusions This paper proposes the Hyperbolic Heterogeneous Hypergraph Neural Network (HHexGNN) to address the challenges of high-order interaction modeling and hierarchical structure preservation in heterogeneous graphs. By integrating meta-path-induced hypergraph structures with hyperbolic manifolds, the model effectively enhances the representation capability for complex non-Euclidean topological relationships. HHexGNN significantly outperforms existing baseline models across all datasets and various training ratios in classification, clustering, and link prediction tasks. Specifically, the model achieves a 4.27% improvement in Micro-F1 on the IMDB dataset and a 5.51% increase in ARI on the ACM dataset, with a maximum performance gain of 10.43% in core metrics. Notably, HHexGNN exhibits exceptional data efficiency; its performance with only 20% training data surpasses that of most baselines trained on 80% of the data. These results validate the effectiveness of the model in capturing latent semantic structures and non-Euclidean topologies. Future research will explore adaptive meta-path generation and native hyperbolic algorithms to mitigate potential information loss caused by pre-defined paths and tangent space projections, thereby further refining representation precision in complex networks.
- Heterogeneous Graph,
- Hyperbolic Space,
- Hypergraph,
- Attention Mechanism,
- Node Representation Learning

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