An automated Neurological Disorder detection system can be considered as a cost-effective
and resource efficient tool for medical and healthcare applications. In automated …

Graph data are pervasive in real-world scenarios, and research on graph data has become
a research hotspot. Over the past few decades, significant advancements have been made …

In this work, we propose two novel quantum walk kernels, namely the Hierarchical Aligned
Quantum Jensen-Shannon Kernels (HAQJSK), between un-attributed graph structures …

Knowledge tracing (KT) is an intelligent educational technology used to model students'
learning progress and mastery in adaptive learning environments for personalized …

While highly influential in deep learning, especially in natural language processing, the
Transformer model has not exhibited competitive performance in unsupervised graph …

The application of contrastive learning (CL) to collaborative filtering (CF) in recommender
systems has achieved remarkable success. CL-based recommendation models mainly …

The graph-based multi-view methods have achieved state-of-the-art results in 3D shape
analysis tasks by taking advantage of graph convolutional networks (GCN) to process …

In order to improve the classification performance of the model for different kinds of
arrhythmias, a parallel classification model of arrhythmia based on DenseNet-BiLSTM is …

Brain network, which characterizes the functional and structural interactions of brain regions
with graph theory, has been widely utilized to diagnose brain diseases, such as autism …

In recent years, the results of view-based 3D shape recognition methods have saturated,
and models with excellent performance cannot be deployed on memory-limited devices due …