Graph-Based Event and Sub-Event Grouping in User-Generated Videos with Distortion-Aware Keyframe Clustering

User-generated content (UGC) videos recorded in uncontrolled environments often exhibit blur, camera shake, lighting fluctuations, and large viewpoint differences, making it difficult to organize multiple recordings of the same event. This work proposes an integrated pipeline that groups UGC videos into coherent events and sub-events by combining distortion-aware keyframe selection, adaptive audio–visual fusion, and confidence-weighted graph construction. The method first filters and clusters segment-level representations to obtain reliable keyframes, then fuses audio and visual cues through a lightweight gating module to produce robust multimodal descriptors. These descriptors populate a similarity graph whose strong and weak edges reveal sub-event and event structure without requiring shot boundaries or manual segmentation. Although distortion modeling, keyframe extraction, and multimodal similarity have been studied separately, existing approaches do not integrate them for hierarchical UGC video grouping. Experiments on the JIKU dataset and a curated YouTube dataset show consistent improvements in fidelity, diversity, and clustering metrics, demonstrating the applicability of the approach to video summarization, multi-view organization, and other UGC analysis tasks.