HeHLP-HHAN: Heterogeneous Hyperlink Prediction method based on Hyperbolic Hypergraph Attention Network

Heterogeneous hypergraphs can better reflect real events, and heterogeneous hyperlink prediction is an effective way to discover hidden events. Most existing methods embed heterogeneous hypergraphs in Euclidean space for representation learning. However, distortion occurs when embedding graph-structured data in Euclidean space, which affects prediction performance. To tackle these challenges, we introduce hyperbolic hypergraph embedding to reduce distortion. In this paper, we propose a novel Heterogeneous Hyperlink Prediction method based on Hyperbolic Hypergraph Attention Networks (HeHLP-HHAN). First, the feature spaces of different types of nodes are aligned. Next, the hypergraph convolution operation in hyperbolic space is designed to obtain the hyperbolic embedding of nodes. Finally, a hyperlink scoring function is designed based on the hyperbolic embedding to measure the likelihood of the hyperlink existence. In addition, we design two attention mechanisms, type-level and node-level, to improve the expression ability of the HeHLP-HHAN model. In experiments on five real-world network event datasets, HeHLP-HHAN outperforms baselines in terms of Average Precision (AP) and Area Under Curve (AUC) indicators, achieving the best prediction performance.