9 papers
A Unified Framework for Context-Aware and Relation-Aware Graph Retrieval-Augmented Generation
Haoyang Zhong, Yifei Sun, Antong Zhang +3
Retrieval-Augmented Generation (RAG) has emerged as a paradigm for enhancing large language models (LLMs) with external knowledge, yet existing graph-based methods face a fundament…
Handling Feature Heterogeneity with Learnable Graph Patches
Yifei Sun, Yang Yang, Xiao Feng +4
In recent years, the rapid development of foundation models and graph pre-training technologies has spurred increasing interest in constructing a universal pre-trained graph model…
PTCG-Bench: Can LLM Agents Master Pokémon Trading Card Game?
Dongdong Hua, Yifei Sun, Renhong Huang +3
Given a strategically complex board game, human players can quickly learn to devise strategies after playing a few rounds. Autonomous agents require similar capabilities in realist…
UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure
Taoran Fang, Yan Deng, Chunping Wang +3
With the rapid growth of digital data, real-world applications increasingly involve hierarchical information that combines static attributes with dynamic records. Modeling such het…
Compositional Feature Augmentation for Unbiased Scene Graph Generation
Lin Li, Guikun Chen, Jun Xiao +3
Scene Graph Generation (SGG) aims to detect all the visual relation triplets \texttt{sub}, \texttt{pred}, \texttt{obj} in a given image. With the emergence of various advance…
How to Use Graph Data in the Wild to Help Graph Anomaly Detection?
Yuxuan Cao, Jiarong Xu, Chen Zhao +4
In recent years, graph anomaly detection has found extensive applications in various domains such as social, financial, and communication networks. However, anomalies in graph-stru…