Abstract: Hypergraph neural networks (HGNNs) are widely used models for analyzing higher-order relational data. HGNNs suffer from the rapid performance degradation with increasing layers. Hypergraph ...

Abstract: With the increasing variety and widespread adoption of consumer electronic products, their operational failures have an increasingly significant impact on daily life. Traditional fault ...

Graph representation learning (GRL) has emerged as an effective technique for modeling graph-structured data. When modeling the heterogeneity and dynamics in real-world complex networks, GRL methods ...

Early detection of Alzheimer’s disease (AD) through spontaneous speech analysis represents a promising, non-invasive diagnostic approach. Existing methods predominantly rely on fusion-based multimodal ...