A contrastive learning framework of graph reconstruction and hypergraph learning for key node identification

With the emergence of complex networks in various domains, the key node identification has become one of the critical issues that needs to be studied. Traditional index methods typically focus on single structural information, while existing data-driven approaches often rely solely on the intrinsic lower-order features of nodes. However, assessing the importance of nodes requires a comprehensive consideration of both network structures and higher-order features from multiple perspectives. To address these challenges, this paper proposes a novel deep learning framework based on Graph Reconstruction and Hypergraph Contrastive Learning, termed GRHCL. The GRHCL method constructs hypergraph structures from original graphs using random walks, followed by leveraging graph reconstruction and hypergraph learning methods to capture both structural and higher-order embedding features of nodes. Positive and negative node pairs are then constructed across different views for contrastive learning. Finally, the model is trained using a training sample set obtained through a clustering sampling strategy, along with a joint loss function. Comparative experiments against various baseline methods demonstrate that the GRHCL method achieves superior predictive performance with smaller training sets, improving accuracy by over 5% on some datasets compared to the next best-performing method.