A Quantum Spatial Graph Convolutional Network for Text Classification

Abstract

The data generated from non-Euclidean domains and its graphical representation (with complex-relationship object interdependence) applications has observed an exponential growth. The sophistication of graph data has posed consequential obstacles to the existing machine learning algorithms. In this study, we have considered a revamped version of a semi-supervised learning algorithm for graph-structured data to address the issue of expanding deep learning approaches to represent the graph data. Additionally, the quantum information theory has been applied through Graph Neural Networks (GNNs) to generate Riemannian metrics in closed-form of several graph layers. In further, to pre-process the adjacency matrix of graphs, a new formulation is established to incorporate high order proximities. The proposed scheme has shown outstanding improvements to overcome the deficiencies in Graph Convolutional Network (GCN), particularly, the information loss and imprecise information representation with acceptable computational overhead. Moreover, the proposed Quantum Graph Convolutional Network (QGCN) has significantly strengthened the GCN on semi-supervised node classification tasks. In parallel, it expands the generalization process with a significant difference by making small random perturbations ÁG of the graph during the training process. The evaluation results are provided on three benchmark datasets, including Citeseer, Cora, and PubMed, that distinctly delineate the superiority of the proposed model in terms of computational accuracy against state-of-the-art GCN and three other methods based on the same algorithms in the existing literature.

The Convolutional Neural Network Text Classification Algorithm in the Information Management of Smart Tourism Based on Internet of Things

“…4), i, j, m, and n denote distinct parameters. I represents the image, while K stands for the two-dimensional kernel [15][16][17]. By leveraging the commutative property of convolution, Equation ( 4) can alternatively be expressed as Equation (5).…”

“…Precision quantifies the ratio of correctly categorized positive samples to the total number of samples predicted as positive by the classifier. This metric provides valuable insights into the model's predictive precision, and its calculation is outlined in Equation (15). Precision = (15) Recall, often referred to as sensitivity, measures the proportion of accurately classified positive samples in relation to the total number of actual positive samples.…”