Research On Graph Neural Network Models For Practical Applications

Graph Neural Network(GNN)refers to a neural network-based approach for learning from graph-structured data,allowing the extraction and exploration of features and patterns inherent in the graph structure.GNN has gained widespread application in various domains,including drug discovery,neuroscience,life sciences,and social networks.The fundamental idea behind GNN lies in leveraging the graph structure and employing aggregation techniques to learn representations of nodes,edges,and the entire graph,enabling tasks such as node classification,edge prediction,and graph classification.However,the efficacy of GNN heavily relies on the completeness of the graph structure and its susceptibility to noise interference.In practical scenarios,graph structures are often incomplete and subject to significant noise interference,posing challenges such as high computational complexity.To address the aforementioned challenges encountered in real-world applications,this paper focuses on two key issues in graph neural networks:incomplete graph structures and high computational complexity in graph frequency domain methods.The contributions of this research are as follows:Robust Graph Structure Learning based on Virtual Node Construction: This study proposes a robust graph structure learning method that tackles the issues of incomplete graph structures and noise interference.Adopting a graph construction perspective,the method incorporates virtual node construction to enhance the robustness and completeness of the graph structure.Four aspects are considered: edge discovery,edge removal,reweighting of existing edges,and differentiability of the graph structure.Virtual nodes are introduced,and their connections are utilized to optimize the graph structure.Additionally,the Gumbel-Softmax method is employed to reweight edges,ensuring differentiability of the graph structure.Experimental evaluations on various datasets demonstrate the superiority of the proposed method compared to alternative approaches.For instance,in the task of node classification on the Cora citation network dataset,the proposed method achieves a 3.4%improvement in classification accuracy compared to other methods,while consuming only 25% of the memory and runtime of the Graph Attention Networks(GAT).Furthermore,extensive experiments considering different attack types and noise levels confirm the robustness of the proposed method.Fast Learning Algorithm for Graph Convolutional Networks based on Iteration and Vandermonde Matrices: This paper addresses the trade-off between computational complexity and the spatial receptive field in graph convolutional neural network algorithms.To this end,a fast learning algorithm for graph convolutional networks is proposed,leveraging iteration and Vandermonde matrices.The algorithm effectively aggregates Kth-order neighbor information of nodes while reducing computational complexity without sacrificing efficiency and performance.In semi-supervised node classification tasks on benchmark datasets such as Cora,Citeseer,and Pubmed,the proposed algorithm outperforms existing methods in terms of classification accuracy.Notably,it demonstrates more than a three-fold increase in runtime speed compared to Graph Attention Networks(GAT)while consuming six times less memory.In conclusion,this paper presents VN-GSL and IGN models as effective solutions for addressing two significant challenges in graph neural networks,namely robustness to noisy input graphs and high computational complexity.The experimental results highlight the competitive performance and efficiency of these models on multiple benchmark datasets,underscoring their potential in practical applications.