## 1 Introduction
A contrastive loss function is designed to train the model, which maximizes the similarity between different views of the same graph and minimizes the similarity between views of different graphs. Common contrastive loss functions include InfoNCE Loss [15] and Contrastive Loss. InfoNCE Loss aims to increase the similarity between different views of the same graph and decrease the similarity between views of different graphs, while Contrastive Loss aims to minimize the distance between positive sample pairs and maximize the distance between negative sample pairs. During training, model parameters are adjusted through backpropagation and gradient descent algorithms to minimize the contrastive loss. This enables the model to distinguish similar and dissimilar structural features in the graph, thereby learning the structural feature information of the graph data in an unsupervised manner. This study analyzes the limitations of lightweight graph neural networks through knowledge distillation and examines the structural representation capability of graph contrastive learning technology. Based on this analysis, we propose a lightweight method for graph neural networks that combines knowledge distillation with graph contrastive learning. Specifically, the graph contrastive learning technique is first utilized to extract structural features from graph data, which are then concatenated with node content features. The concatenated node features are enhanced and then fed into an MLP. Then, knowledge distillation with variable temperature is employed to transfer knowledge from the teacher model, where the soft label outputs serve as supervisory signals for training the student model, thus facilitating knowledge transfer. To enhance the robustness of the student model MLP, an adversarial feature enhancement strategy is employed to minimize the impact of noise on the accuracy and stability of the MLP. This enables the generation of a noise-resistant, high-efficiency, and high-performance student MLP model, which we call KDGCL. In order to comprehensively evaluate our proposed model, extensive experiments were conducted on seven datasets in both transductive (tran) and inductive (ind) settings. We concluded that our model learns through integrating graph structure feature information, where feature enhancement and knowledge distillation can improve the performance and robustness of the student model MLP. The contributions of the proposed method are summarized as follows:  
1. This study proposes a lightweight graph neural network method that combines knowledge distillation and graph contrastive learning. By integrating graph contrastive learning and feature enhancement, we can efficiently distill the knowledge from the GNN and transfer it to a student model. 2. The effectiveness and robustness of the graph contrast learning module and feature enhancement module are proven using seven public datasets, three teacher model architectures, and two experimental conditions. The experimental results demonstrate that the KDGCL model outperforms NOSMOG on six out of seven datasets. 3. The variable temperature module we designed enables the temperature to change with the progress of distillation. Compared with using a globally unified temperature, the student MLP obtained with distillation has better performance and stronger stability.