Although graph neural networks (GNNs) have achieved impressive achievements in graph
classification, they often need abundant task-specific labels, which could be extensively …

Efficiently capturing consistent and complementary semantic features in a multimodal
conversation context is crucial for Multimodal Emotion Recognition in Conversation (MERC) …

Multi-modal Emotion Recognition in Conversation (MERC) has received considerable
attention in various fields, eg, human-computer interaction and recommendation systems …

Graph neural networks (GNNs) have achieved impressive impressions for graph-related
tasks. However, most GNNs are primarily studied under the cases of signal domain with …

Amid the rise of mobile technologies and Location-Based Social Networks (LBSNs), there's
an escalating demand for personalized Point-of-Interest (POI) recommendations. Especially …

In recent years, deep learning on graphs has achieved remarkable success in various
domains. However, the reliance on annotated graph data remains a significant bottleneck …

Continuous graph neural networks (CGNNs) have garnered significant attention due to their
ability to generalize existing discrete graph neural networks (GNNs) by introducing …

Accurate and efficient safety assessment for train-bridge coupled (TBC) systems is
paramount in railway engineering. Traditional neural networks, though efficient and apt for …

Graph Neural Networks (GNNs) have been increasingly deployed in a plethora of
applications. However, the graph data used for training may contain sensitive personal …

Graph neural networks (GNNs) have achieved great success recently on graph classification
tasks using supervised end-to-end training. Unfortunately, extensive noisy graph labels …