Semi-supervised heterogeneous graph contrastive learning with label-guided

Heterogeneous Graph Neural Networks represent a powerful approach to understand and utilize the intricate structures and semantics within complex graphs. When it comes to semi-supervised learning on graphs, the challenge lies in effectively leveraging labeled data to generalize predictions to unlabeled nodes. Traditional methods often fall short in fully utilizing labeled information, limiting their performance to the number of available labels. To overcome these limitations, in this paper, we propose a Semi-Supervised Heterogeneous Graph Contrastive Learning with Label-Guided (SSGCL-LG) model. SSGCL-LG tackles this challenge by fully integrating label information into the learning process through contrastive learning. Specifically, it constructs a label graph that incorporates both node and label representations, enhancing the supervised signal. Moreover, we propose a novel strategy for selecting positive and negative samples based on labels and meta-paths, effectively pulling positive samples closer together in the embedding space. To optimize node representations, SSGCL-LG combines contrastive loss with semi-supervised loss, enabling the model to learn from both labeled and unlabeled data. Extensive experiments on real-world datasets validate the effectiveness of our framework, demonstrating its superiority over existing methods. The code for this work is publicly available in the https://github.com/sun281210/SSGCL-LG.