Research On Multi-Channel Graph Neural Networks For Graph Data

Graph data is a complex data type characterized by its unstructured nature,making it challenging to represent using simple tables or vectors.At the core of graph data are nodes and edges,representing entities and their relationships,respectively.This representation allows graph data to more naturally and accurately describe complex relationships in the real world.Graph data finds wide applications in various domains such as social network analysis,bioinformatics,and recommendation systems,among others.To better understand and process graph data,Graph Neural Networks(GNNs)have gained widespread attention as deep learning models specialized in handling graph data.The key idea behind graph neural networks is to aggregate and propagate information from nodes and edges to obtain richer representation capabilities.By iteratively updating node representations and utilizing information from neighboring nodes for information propagation and aggregation,GNNs integrate neighborhood information into the representation of each node.However,traditional single-channel graph neural networks often capture only local graph data features,neglecting the diversity and complexity of graph data.This can lead to the loss of important information during data processing,and most graph neural network methods require a large amount of labeled data for training.To address these issues,this paper proposes a multi-channel graph neural network for graph data,aiming to learn feature representations of graph data from multiple perspectives and scales and achieve self-supervised multi-channel graph neural networks.The main contributions and innovations of this paper are as follows:(1)Addressing the problem of traditional single-channel graph neural networks focusing only on a single feature of graph data,a multi-channel graph neural network method based on attention mechanisms is proposed.This method emphasizes leveraging multiple feature information of graph data and fusing these features through attention mechanisms to explore common and complementary hidden features of nodes under different relationship features.(2)To mitigate overfitting,oversmoothing,and feature interaction issues during the training process of single-channel graph neural networks,a multi-channel deep graph neural network method based on degree matrix optimization is proposed.This method optimizes the graph convolution function and introduces residual connections within and between channels to alleviate overfitting and oversmoothing issues of deep models.The network is horizontally deepened to gradually extract advanced features of nodes and vertically widened to promote interaction between channels,enhancing the model’s ability to learn different relationship features.(3)Addressing the problem of single-channel graph neural networks overlooking graph structure diversity,a multi-channel self-supervised graph neural network method based on line graph structure is proposed.This method introduces line graph structures to better integrate node features and graph structural information,and designs a multi-angle contrastive learning loss function that combines node feature fusion and spectral graph feature fusion to control self-supervised learning,improve downstream task performance,and achieve better interpretability.(4)To tackle the adaptability problem of single-channel graph neural networks in capturing global graph information and lacking local and global information,a multichannel self-supervised graph neural network method based on local and global perspectives is proposed.This method combines multiple channel graph representation learning methods to extract global and local information from multiple subgraph views,with each channel focusing on different subgraph views to comprehensively capture graph feature expressions.Furthermore,an adaptive collaborative optimization mechanism is designed to enhance the model’s expression and generalization capabilities through adaptive collaboration optimization of global perspective contrastive loss and local perspective contrastive loss.(5)Addressing the problem of single-channel graph neural networks paying less attention to high-order features and mixed-order features,a multi-channel selfsupervised graph neural network method based on mixed-order feature contrast is proposed.This method explores graph structure optimization based on causal relationships,uses causal reasoning models to optimize input graphs,generate more expressive graph structures,and improve graph data representation capabilities.It introduces a multi-level mixed-order neighborhood learning mechanism to encode high-order neighborhood information such as first-order,second-order,and third-order.A loss function based on same-order and mixed-order feature contrast is designed to compare node representations under same-order and mixed-order to capture differentorder neighborhood information and achieve more comprehensive and rich node representations.