篇名：Multi-view heterogeneous graph learning with compressed hypergraph neural networks
作者：Aiping Huang, Zihan Fang, Zhihao Wu, Yanchao Tan, Peng Han, Shiping Wang, Le Zhang
年份：2024
DOI：https://doi.org/10.1016/j.neunet.2024.106562
文章摘要：
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.
关键词：
Multi-view learning Heterogeneous graph Hypergraph convolution Graph neural network
