Hyperedge prediction plays a critical role in high-order relational modeling with hypergraphs, yet most existing methods primarily focus on sampling strategies or local aggregation within candidate hyperedges. These approaches often overlook global structural dependencies that are essential for learning expressive node and hyperedge representations. In this paper, we propose HyperNoRA, a novel self-supervised hypergraph learning framework that integrates global node-level relation awareness with contrastive learning. Specifically, we construct a global node relation graph that captures both direct and indirect structural correlations, which guides a structure-aware aggregator to enhance node representations with informative global context. To prevent over-smoothing and maintain discriminability, a contrastive learning module is introduced to align representations across graph augmentations while separating semantically dissimilar nodes. Extensive experiments on several benchmark datasets demonstrate that HyperNoRA consistently outperforms state-of-the-art baselines, and ablation studies verify the effectiveness of its key components.

Accurate emotion detection in conversations using multimodal features is essential for effective human-computer interaction. There are three pivotal aspects in multimodal emotion recognition in conversation (MERC), i.e., intricate temporal information, modality interactions (both intra- and inter-modal) and implicit high-order linguistic cues within dialogues. Existing approaches are limited to the first two, hindering the generation of effective emotional representations. To this end, we propose HIGH, a hierarchical graph learning framework designed for MERC. HIGH enhances the perception of low-level information (temporal and intra-modal information) by constructing directed dialogue graphs for each modality. A dynamic multimodal filtering mechanism and a modality-aligned contrastive learning approach further refine the semantic nuances. Additionally, the constructed speaker-centered hypergraph yields high-level information like cross-modal interactions and high-order linguistic cues between utterances. HIGH effectively integrates fundamental low-level information with high-order details in a hierarchical manner, considering all three key factors simultaneously. Extensive experiments on two benchmark datasets demonstrate the effectiveness and superiority of HIGH.