Shirui Pan

New method detects anomalies in time series data despite missing values.

This paper discusses the role of LLMs in time series analysis.

This article discusses distribution shifts and challenges in graph learning.

A new method to fine-tune models while ensuring data privacy.

Learn how diffusion models enhance time series data analysis across various fields.

An overview of NMF and its applications in simplifying complex data.

New method improves data removal in dynamic graph models while ensuring privacy.

A method that customizes graph sparsification for better neural network performance.

ARC provides a flexible solution for detecting anomalies across various graph datasets.

Examining the challenges and solutions in LLM watermarking to prevent misuse.

A new approach enhances the generation of molecules for drug response prediction.

A new perspective on enhancing GNNs for complex graph structures.

A new framework combines language models and knowledge graphs for clearer product suggestions.

A new method enhances learning from graphs despite data noise.

A look at how TTDS improves personalized recommendations using large language models.

DA-MoE adapts GNNs for varying graph sizes, improving performance across diverse tasks.