Research On Graph Neural Network Model For Node Classification

Graph-structured data are ubiquitous in the real world,such as citation networks,social networks,web networks,and biological networks,and they contain rich information to be mined.Correspondingly,graph mining aims to mine the correlations among graph topology,node features,and edge features,and this has been proven to be very successful in downstream tasks such as node classification,node clustering,and link prediction.Among them,the research of graph neural network model for node classification is a direction worthy of further study.Node classification is usually given the class of some nodes in the graph,and then predicts the class of unlabeled nodes in the graph through traditional graph representation learning methods or graph neural networks.In recent years,graph representation learning and graph neural networks have become popular research directions in the field of graph mining.With the development of deep learning and the explosion of graph neural network representation capabilities,a large number of works have proposed graph neural networks based on deep learning when dealing with the above tasks of graph representation learning.According to whether the convolution is applied to the spectral domain or the spatial domain,the research progress of graph neural networks is usually divided into spectral approaches and spatial approaches.However,considering the neighborhood aggregation,model depth and computational efficiency of the graph neural network,when designing the graph neural networks,there are the following challenges:(1)For spectral approaches,how to simplify the neighborhood aggregation process,while improving the model depth and alleviating the oversmoothing to improve the model classification performance?(2)For spatial approaches,when extracting node features,how to introduce the influence of all nodes without changing the graph structure to improve the feature aggregation ability of the model?(3)In view of the gap between spectral approaches and spatial approaches,how to design a hybrid model to integrate the advantages of spectral approaches and spatial approaches?Given the above problems,the main research contents and innovations of this dissertation include the following aspects:(1)Simple Jumping Knowledge Network(SJK-Net)is proposed.SJK-Net is a novel neural network model for processing graph-structured data.In this dissertation,we first implement neighborhood aggregation with a simple no-learning approach,which not only aggregates the feature representations of each node quickly and efficiently,but also takes up less storage space.Then,in the last layer,we utilize Jumping Knowledge Networks(JK-Net)to combine the features of different layers,so that all node feature representations jump to the last layer.In this case,SJK-Net improves the depth of the model and can quickly learn the subgraph feature representations corresponding to different neighborhood ranges of nodes,thus alleviating the problem of oversmoothing.Extensive experiments on citation networks and social networks demonstrate that the proposed model matches or outperforms state-of-the-art methods on the node classification tasks.(2)Full Graph Attention Neural Network(FGANN)is proposed.FGANN leverages an attention mechanism to introduce the influence of all nodes when computing the hidden feature representation of each node.First,we define the similarity between two node features as the attention coefficient,and then calculate the attention coefficient between any two nodes through the self-attention mechanism.Second,a simple and effective technique,the softmax function,is applied to the attention matrix to inject the influence of non-neighborhood nodes into in-neighborhood nodes.Finally,we again focus on the nodes in the neighborhood,and use masked attention to introduce the graph structure(adjacency matrix)and(implicitly)assign different weights to different nodes in the neighborhood,thus constructing the renormalization adjacency matrix.Under these circumstances,FGANN not only addresses several important challenges of spatialbased graph neural networks,but also handles node classification tasks efficiently.In this case,FGANN considers the mutual influence among all nodes and utilizes full-graph attention to construct a new adjacency matrix,which can handle the node classification task more efficiently.Extensive experiments on the citation networks demonstrate that the proposed FGANN model can match or outperform state-of-the-art methods on the node classification tasks.(3)Hybrid Deep Graph Convolutional Network(HDGCN)is proposed.HDGCN is a novel deep GCN model that combines spectral and spatial approaches.First,this dissertation defines a simple and effective method to combine spectral and spatial approaches.To take advantage of this combination,we introduce it into a deep GCN model.HDGCN calculates the adjacency matrix using spectral method and spatial method respectively,and then combines these two adjacency matrices to generate a new adjacency matrix.The new adjacency matrix contains richer graph structure information,which is beneficial to extract more accurate node feature representation.In addition,HDGCN utilizes initial residual and identity mapping to alleviate the oversmoothing problem in deep GCN models.In this case,HDGCN bridges the gap between spectral and spatial methods from the perspective of combining adjacency matrices,and exhibits the advantage of deep GCN models to extract more accurate node feature representation.Extensive experiments on citation networks and web networks provide evidence that the proposed model matches or outperforms state-of-the-art methods on the node classification tasks.In summary,for the node classification tasks,this dissertation conducts in-depth and detailed research on the graph neural network models from three aspects: spectral approaches,spatial approaches and hybrid approaches.The experimental results show that the proposed graph neural networks can effectively improve the operating efficiency,improve the depth of the model,and alleviate the oversmoothing problem,so that it can efficiently handle the node classification tasks.