DiffTSP: A Conditional Discrete Diffusion Model for Knowledge Graph Triple Set Prediction
DiffTSP is a discrete diffusion model that treats knowledge graph triple prediction as a generative task to capture inter-triple dependencies. It uses a structure-aware denoising network with a Relational Context Encoder and Graph Diffusion Transformer to generate missing facts holistically. This approach outperforms baselines on Wiki79k, Wiki143k, and CFamily by modeling structural interactions effectively.