Adaptive Gated Simplicial Propagation for Node Classification in Multimodal Graphs

Node classification in multimodal graphs plays an important role in many real-world applications, where nodes are described by multiple modalities such as text and images, and the graph structure captures relations among entities. However, most existing Graph Neural Networks (GNNs) still focus on pairwise connections, overlooking higher-order relational patterns. Recent studies have explored simplicial complexes to capture higher-order interactions and integrated them into GNN frameworks. Motivated by this line of work, we propose Adaptive Gated Simplicial Propagation (AGSP), an end-to-end framework for node classification in multimodal graphs. Specifically, AGSP first introduces a simplicial propagation layer to capture higher-order relations beyond pairwise connections; then it applies an adaptive gating mechanism to balance multi-order topological relations, enhancing the discriminative ability of node classification. Extensive experiments validate that AGSP generally outperforms the state-of-the-art baselines, highlighting the effectiveness of combining multimodal information with higher-order graph topology through adaptive gated fusion.