graph-attention-network

This paper proposes HCIG, a hierarchical cross-modal incongruity graph network for multimodal sarcasm and cyberbullying detection, achieving state-of-the-art performance on the MMSD and MultiBully datasets.

This paper presents a comprehensive comparison of deep learning architectures, including Vision Transformers and Graph Attention Networks, for automated sleep apnea detection from multichannel EEG signals, achieving a best test AUC of 0.750 using a vision transformer trained on topological data analysis features.

This paper proposes HIA-GAT, a dual-stream heterogeneous graph attention network that integrates longitudinal and lateral vehicle interactions with a conflict-type-aware gating mechanism for frame-level traffic conflict risk prediction on freeways. Experiments on NGSIM datasets show improved risk-ranking performance, particularly for lateral conflicts, and provide interpretable per-vehicle conflict attribution.