<p>This paper presents a novel multi-view multimodal graph learning framework for distributed audio-visual event classification using synchronized sequences from multi-microphone and multi-camera sensors. Existing approaches often rely on simple aggregation strategies for multi-view multimodal inputs, which fail to adequately capture the complex spatio-temporal relationships both within and across modalities. To address this limitation, we propose a graph attention network architecture with individual frame-level sensor nodes for each microphone and camera, and three types of frame-level spatio-temporal nodes. In this framework, within each temporal frame, audio spatio-temporal nodes connect to microphones, video spatio-temporal nodes to cameras, and audio-video spatio-temporal nodes to all sensor nodes. Temporal edges further interconnect each spatio-temporal node with its corresponding node in preceding frames. This architecture enables dynamic aggregation of sensor node features through attention-weighted mechanisms, generating updated spatio-temporal nodes that capture intra-modal, inter-modal, intra-frame, and inter-frame relational dependencies. Experimental results on the MM-Office and MM-OR datasets demonstrate that the proposed framework significantly outperforms existing baseline methods for audio-visual event classification, highlighting its superior capability in modeling complex spatio-temporal dependencies across distributed sensor networks.</p>