Generative-Enhanced Heterogeneous Graph Contrastive Learning

• URL: http://arxiv.org/abs/2404.02810v2
• Date: Wed, 8 May 2024 01:40:25 GMT
• Title: Generative-Enhanced Heterogeneous Graph Contrastive Learning
• Authors: Yu Wang, Lei Sang, Yi Zhang, Yiwen Zhang,
• Abstract summary: Heterogeneous Graphs (HGs) can effectively model complex relationships in the real world by multi-type nodes and edges. In recent years, inspired by self-supervised learning, contrastive Heterogeneous Graphs Neural Networks (HGNNs) have shown great potential by utilizing data augmentation and contrastive discriminators for downstream tasks. We propose a novel Generative-Enhanced Heterogeneous Graph Contrastive Learning (GHGCL)
• Score: 11.118517297006894
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Heterogeneous Graphs (HGs) can effectively model complex relationships in the real world by multi-type nodes and edges. In recent years, inspired by self-supervised learning, contrastive Heterogeneous Graphs Neural Networks (HGNNs) have shown great potential by utilizing data augmentation and contrastive discriminators for downstream tasks. However, data augmentation is still limited due to the graph data's integrity. Furthermore, the contrastive discriminators remain sampling bias and lack local heterogeneous information. To tackle the above limitations, we propose a novel Generative-Enhanced Heterogeneous Graph Contrastive Learning (GHGCL). Specifically, we first propose a heterogeneous graph generative learning enhanced contrastive paradigm. This paradigm includes: 1) A contrastive view augmentation strategy by using a masked autoencoder. 2) Position-aware and semantics-aware positive sample sampling strategy for generating hard negative samples. 3) A hierarchical contrastive learning strategy for capturing local and global information. Furthermore, the hierarchical contrastive learning and sampling strategies aim to constitute an enhanced contrastive discriminator under the generative-contrastive perspective. Finally, we compare our model with seventeen baselines on eight real-world datasets. Our model outperforms the latest contrastive and generative baselines on node classification and link prediction tasks. To reproduce our work, we have open-sourced our code at https://anonymous.4open.science/r/GC-HGNN-E50C.

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