## 1 Introduction  
Graph neural networks (GNNs) have shown great potential in processing non-Euclidean data and have achieved good performance in various graph machine learning tasks [1]. The success of modern graph neural networks relies on the message passing scheme. This scheme's core principle is to center on each node, aggregate information from its neighboring nodes, and combine it with the node's own representation to update its representation [1]. Due to the node dependency of graph data and the inherent limitations of the message passing scheme [2; 3; 4], deploying graph neural networks on end-devices with restricted computational and storage resources poses a significant challenge. This difficulty hinders the application of graph neural networks in real-world scenarios for rapid and high-performance inferencing. To meet the demands for quick inference in real-world scenarios, the multilayer perceptron (MLP) remains the optimal choice, although it performs poorly on non-Euclidean data [3]. Graph neural networks have good performance advantages on graph-structured data. Although the MLP performs poorly on graph data, its computational efficiency is much higher than that of GNNs due to its simple structure, ease of computing node outputs and updating parameters, and the highly parallelizable nature of fully connected layer computations. To fully leverage the performance advantages of GNNs and the efficiency advantages of MLPs, some studies have combined both into a single framework [3; 4; 5]. A mainstream method for combining GNN and MLP models is knowledge distillation, which uses the soft label (i.e., probability distribution) outputs of the teacher model (in this case, the GNN) as supervision signals to train the student model (the MLP) and complete the transfer of knowledge from the teacher model to the student model [3]. The distilled student model MLP is then deployed for fast inference. The MLP model only inputs the content features of nodes and does not include graph structure features. Through knowledge distillation, the student model MLP can imitate the ability of the teacher model GNN, while its inference speed is much faster than that of the GNN. However, this approach has the following two drawbacks: first, the student model MLP is unable to directly capture graph structural information and can only utilize node content features as input; second, the accuracy of the student model MLP is far inferior to that of the teacher model GNN, and it is more sensitive to noise and interference in graph data. There are two primary methods for lightweight graph neural networks through knowledge distillation. In one approach, both the teacher and student models are GNNs [6; 7; 8; 9], named GNN-to-GNN. It involves transferring knowledge from a large, complex teacher model to a smaller, more efficient student model through knowledge distillation. GNN-to-GNN distillation reduces the parameter scale of the GNN model and decreases inference time by reducing the number of layers and neurons in the student model; however, it does not fundamentally address the issue of node dependency during the inference process. Consequently, it fails to effectively improve inference efficiency on large-scale graph datasets. The other approach is heterogeneous knowledge distillation, in which the teacher model is a GNN and the student model is an MLP or another model [3; 4; 10; 11; 12]. The output probability distribution of the GNN guides the training of the MLP, allowing the MLP to imitate the GNN, thus effectively transferring its knowledge. Graph contrastive learning is an unsupervised learning method that aims to learn the structural features of graph data without requiring any label information [13]. The core idea of this approach is to train the model by generating different views of the graph, thereby learning the intrinsic structure of the graph and the relationships between nodes. This is achieved by creating two or more \"views\" of the graph by adding or deleting nodes or edges, as well as perturbing attributes [14].