## 2 Related Works  
Many graph neural networks [16; 17; 18; 19; 20] have been specifically proposed for processing graph-structured data. Most of these graph neural networks follow the message passing paradigm scheme, which aggregates neighbor node information to update node representations [21]. Graph convolutional neural networks (GCNs) calculate graph node representations through computational propagation rules similar to convolutional neural networks [19]. Graph attention networks (GATs) introduce an attention mechanism to aggregate the feature representation of neighbor nodes with different weights [16]. The GraphSAGE approach samples neighbor nodes and applies an aggregation function to aggregate the sampled node feature information and learn node features, enabling its good scalability [18]. GAEs embed graph nodes into low-dimensional space through the encoder and reconstruct the graph structure through the decoder to learn node representations [22; 23]. DeepGCNs [24] and GCNII [20] introduce residual connections to address the issues of over-smoothing and over-fitting in graph neural networks. However, research has shown that graph neural networks in the message passing scheme can only exploit local graph structures and have been proven not to perform better than WL graph isomorphism tests [17; 25; 26]. Inspired by positional encoding in natural language processing technology, some studies have enhanced the graph learning process through integrating node position information [3; 4; 11], such as Laplacian feature mapping and random walk methods [27; 28; 29; 30], thereby enhancing the node feature encoding ability of graph neural networks. Contrastive learning obtains positive and negative sample pairs through data enhancement and negative sampling and uses an objective function to increase the similarity between positive sample pairs and reduce the similarity between negative sample pairs to obtain discriminative features [15; 31; 32]. While graph data come in various forms and are not easy to label, through contrastive learning, graph node feature representations can be learned. For example, InfoGraph learns node representations by maximizing graph-level representation and mutual information between substructures at different scales [33]. SimGRACE utilizes two GNN models as encoders to acquire two similar views for comparison, demonstrating strong performance in terms of generalization and robustness [14]. GCC uses contrastive learning to learn the structural representation of graphs through inter-network and cross-network subgraph instance discrimination [34]. GraphCL [31] and GraphMAE [23] also use contrastive learning to obtain better graph node representations. Utilizing contrastive learning with graph data can effectively address the issue of graph data labels, enabling the learning of node representations and structural features that are beneficial for diverse downstream tasks. The acceleration and compression of GNNs have attracted the attention of many researchers [3; 4; 5; 6; 7; 8; 11; 12]. Common methods include pruning [35], quantification [36; 37; 38],and knowledge distillation (KD) [3; 4; 5; 6; 7; 8; 11; 12]. Among them, KD has been widely used as it can effectively decrease a model's size while increasing its inference speed. Previous works have applied KD to teach student GNNs with fewer parameters but comparable performance to the teacher GNNs. However, time-consuming message passing with multi-hop neighborhood fetching is still required during the inferencing process of the student GNN. For example, while LSP [6] and TinyGNN [7] preserve local structure information in the KD process, neighbor aggregation is still required during message passing, which consumes a lot of time. To address the high-latency issue, recent works have combined the low latency of MLP models with the high performance of GNNs. These approaches involve training an MLP-based student model using knowledge distillation from a complex, high-performing GNN model, such as Graph-MLP [2], GLNN [3], and so on.