Peng Cui

GTAlign achieves superior performance in graph classification tasks without the need for textual data, challenging the reliance on traditional graph neural networks and LLMs.

CDFM outperforms traditional causal discovery algorithms by leveraging a unified framework that adapts to diverse datasets without the need for extensive retraining.

DAG-FM achieves state-of-the-art causal discovery performance by dynamically adapting to diverse causal mechanisms, outperforming both classical algorithms and recent models.