Title: Dynamic–Static Representation Learning with Mamba-Enhanced Diffusion for Temporal Knowledge Graph Reasoning
Primary area: learning on graphs and other geometries & topologies
Keywords: Temporal knowledge graph, Temporal knowledge graph reasoning, Selective State Space Model, Diffusion

Abstract: Temporal Knowledge Graph (TKG) reasoning aims to predict future missing facts based on historical evidence. Prior studies on graph learning and logical rules often overlook global latent semantics and struggle with long-range dependencies, particularly under sparse or unseen facts. To address these limitations, we propose DSEE-MDiff, which frames TKG reasoning as selecting informative history and denoising future signals. Specifically, a Dynamic–Static Entity Selection encoder captures global semantic evolution alongside local structural cues, while a Mamba-based diffusion module injects and removes noise with a selective state-space model to better recover long-range dependencies and mitigate sparsity. The two outputs are fused for prediction through a ConvTransE decoder. Experiments on four public datasets demonstrate that DSEE-MDiff achieves state-of-the-art performance across multiple metrics, validating the effectiveness of the proposed approach.