The pairwise interaction paradigm of graph machine learning has predominantly governed
the modelling of relational systems. However, graphs alone cannot capture the multi-level …

Deep learning (DL)-based intelligent fault diagnosis methods have greatly promoted the
development of the field of fault diagnosis due to their powerful feature extraction ability for …

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …

This paper presents a new approach for assembling graph neural networks based on
framelet transforms. The latter provides a multi-scale representation for graph-structured …

Spectral methods for graph neural networks (GNNs) have achieved great success. Despite
their success, many works have shown that existing approaches are mainly focused on low …

In online learning, personalized course recommendations that align with learners'
preferences and future needs are essential. Thus, the development of efficient recommender …

The nature of heterophilous graphs is significantly different from that of homophilous graphs,
which causes difficulties in early graph neural network (GNN) models and suggests …

Graph Neural Networks (GNNs) have emerged as one of the leading approaches for
machine learning on graph-structured data. Despite their great success, critical …

In this work, we propose a spectral-based graph convolutional network for directed graphs.
The proposed model employs the classic singular value decomposition (SVD) to perform …

In the digital education landscape, cross-modal retrieval (CMR) from multimodal educational
slides represents a significant challenge, particularly because of the complex nature of …