Self-Supervised Nodes-Hyperedges Embedding for Heterogeneous Information Network Learning

The exploration of self-supervised information mining of heterogeneous datasets has gained significant traction in recent years. Heterogeneous graph neural networks (HGNNs) have emerged as a highly promising method for handling heterogeneous information networks (HINs) due to their superior performance. These networks leverage aggregation functions to convert pairwise relations-based features from raw heterogeneous graphs into embedding vectors. However, real-world HINs contain valuable higher-order relations that are often overlooked but can provide complementary information. To address this issue, we propose a novel method called Self-supervised Nodes-Hyperedges Embedding (SNHE), which leverages hypergraph structures to incorporate higher-order information into the embedding process of HINs. Our method decomposes the raw graph structure into snapshots based on various meta-paths, which are then transformed into hypergraphs to aggregate high-order information within the data and generate embedding representations. Given the complexity of HINs, we develop a dual self-supervised structure that maximizes mutual information in the enhanced graph data space, guides the overall model update, and reduces redundancy and noise. We evaluate our proposed method on various real-world datasets for node classification and clustering tasks, and compare it against state-of-the-art methods. The experimental results demonstrate the efficacy of our method. Our code is available at https://github.com/limengran98/SNHE.

Automated summary

The exploration of self-supervised information mining of heterogeneous datasets has gained significant traction in recent years. Heterogeneous graph neural networks (HGNNs) have emerged as a highly promising method for handling heterogeneous information networks (HINs) due to their superior performance. Our proposed method, Self-supervised Nodes-Hyperedges Embedding (SNHE), leverages hypergraph structures to incorporate higher-order information into the embedding process of HINs, demonstrating its efficacy in node classification and clustering tasks.