Multi-view heterogeneous graph learning with compressed hypergraph neural networks

• DOI: 10.1016/j.neunet.2024.106562
• Publication Date: 2024-07-22T15:46:55Z
• Authors: Aiping Huang; Zihan Fang; Zhihao Wu; Yanchao Tan; Peng Han; Shiping Wang; Le Zhang
• Keywords: Machine Learning; Humans; Neural Networks, Computer; Algorithms; Semantics

Multi-view learning is an emerging field of multi-modal fusion, which involves representing a single instance using multiple heterogeneous features to improve compatibility prediction. However, existing graph-based multi-view learning approaches are implemented on homogeneous assumptions and pairwise relationships, which may not adequately capture the complex interactions among real-world instances. In this paper, we design a compressed hypergraph neural network from the perspective of multi-view heterogeneous graph learning. This approach effectively captures rich multi-view heterogeneous semantic information, incorporating a hypergraph structure that simultaneously enables the exploration of higher-order correlations between samples in multi-view scenarios. Specifically, we introduce efficient hypergraph convolutional networks based on an explainable regularizer-centered optimization framework. Additionally, a low-rank approximation is adopted as hypergraphs to reformat the initial complex multi-view heterogeneous graph. Extensive experiments compared with several advanced node classification methods and multi-view classification methods have demonstrated the feasibility and effectiveness of the proposed method.