4 citations · 6 across the 26 of their papers we have counts for
30 papers
One Pretrained Decision Model Is Enough for Training-Free Generalist Graph Anomaly Detection
Xinyu Zhao, Qingyun Sun, Ziwei Zhang +3
Graph anomaly detection (GAD) aims to identify irregular entities in graph-structured data, yet heterogeneous anomaly patterns and distributional differences across graphs challeng…
DualG-MRAG: Decoupling Macro-Reasoning and Micro-Matching for Multimodal Retrieval-Augmented Generation
Jiacheng Tao, Qingyun Sun, Haonan Yuan +2
While Multimodal Retrieval-Augmented Generation (MM-RAG) has shown promising results, it still struggles with complex multi-hop reasoning tasks. Existing methods primarily focus on…
CRITIC-R1: Learning Structured Critics for Retrieval-Augmented Generation
Wenhan Xiao, Ziwei Zhang, Chuanyue Yu +4
Retrieval-augmented generation (RAG) improves knowledge-intensive question answering by incorporating external evidence. However, existing RAG methods still suffer from hallucinati…
EntroAD: Structural Entropy-Guided Prompt Adaptation for Zero-Shot Anomaly Detection
Xinyu Zhao, Qingyun Sun, Jiayi Luo +1
Zero-Shot Anomaly Detection (ZSAD) aims to detect anomalies in unseen domains without target-domain adaptation. Recent CLIP-based methods have shown promising performance by levera…
Is Fixing Schema Graphs Necessary? Full-Resolution Graph Structure Learning for Relational Deep Learning
Yi Huang, Qingyun Sun, Jia Li +2
Relational prediction tasks are fundamental in many real-world applications, where data are naturally stored in relational databases (RDBs). Relational Deep Learning (RDL) addresse…
Decoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models
Haonan Yuan, Qingyun Sun, Junhua Shi +3
Dynamic graphs are ubiquitous in real-world systems, and building generalizable dynamic Graph Foundation Models has become a frontier in graph learning. However, dynamic graphs fro…
AgenticGEO: A Self-Evolving Agentic System for Generative Engine Optimization
Jiaqi Yuan, Jialu Wang, Zihan Wang +3
Generative search engines represent a transition from traditional ranking-based retrieval to Large Language Model (LLM)-based synthesis, transforming optimization goals from rankin…
Zero-shot Generalizable Graph Anomaly Detection with Mixture of Riemannian Experts
Xinyu Zhao, Qingyun Sun, Jiayi Luo +2
Graph Anomaly Detection (GAD) aims to identify irregular patterns in graph data, and recent works have explored zero-shot generalist GAD to enable generalization to unseen graph da…
RAG-GFM: Overcoming In-Memory Bottlenecks in Graph Foundation Models via Retrieval-Augmented Generation
Haonan Yuan, Qingyun Sun, Jiacheng Tao +2
Graph Foundation Models (GFMs) have emerged as a frontier in graph learning, which are expected to deliver transferable representations across diverse tasks. However, GFMs remain c…
Is the Information Bottleneck Robust Enough? Towards Label-Noise Resistant Information Bottleneck Learning
Yi Huang, Qingyun Sun, Yisen Gao +3
The Information Bottleneck (IB) principle facilitates effective representation learning by preserving label-relevant information while compressing irrelevant information. However,…
Privacy Auditing of Multi-domain Graph Pre-trained Model under Membership Inference Attacks
Jiayi Luo, Qingyun Sun, Yuecen Wei +3
Multi-domain graph pre-training has emerged as a pivotal technique in developing graph foundation models. While it greatly improves the generalization of graph neural networks, its…
Towards Effective, Stealthy, and Persistent Backdoor Attacks Targeting Graph Foundation Models
Jiayi Luo, Qingyun Sun, Lingjuan Lyu +4
Graph Foundation Models (GFMs) are pre-trained on diverse source domains and adapted to unseen targets, enabling broad generalization for graph machine learning. Despite that GFMs…
Evolving Graph Learning for Out-of-Distribution Generalization in Non-stationary Environments
Qingyun Sun, Jiayi Luo, Haonan Yuan +4
Graph neural networks have shown remarkable success in exploiting the spatial and temporal patterns on dynamic graphs. However, existing GNNs exhibit poor generalization ability un…
Robust Graph Condensation via Classification Complexity Mitigation
Jiayi Luo, Qingyun Sun, Beining Yang +5
Graph condensation (GC) has gained significant attention for its ability to synthesize smaller yet informative graphs. However, existing studies often overlook the robustness of GC…
GRAVER: Generative Graph Vocabularies for Robust Graph Foundation Models Fine-tuning
Haonan Yuan, Qingyun Sun, Junhua Shi +4
Inspired by the remarkable success of foundation models in language and vision, Graph Foundation Models (GFMs) hold significant promise for broad applicability across diverse graph…
GraphKeeper: Graph Domain-Incremental Learning via Knowledge Disentanglement and Preservation
Zihao Guo, Qingyun Sun, Ziwei Zhang +4
Graph incremental learning (GIL), which continuously updates graph models by sequential knowledge acquisition, has garnered significant interest recently. However, existing GIL app…
Toward a Unified Geometry Understanding: Riemannian Diffusion Framework for Graph Generation and Prediction
Yisen Gao, Xingcheng Fu, Qingyun Sun +2
Graph diffusion models have made significant progress in learning structured graph data and have demonstrated strong potential for predictive tasks. Existing approaches typically e…
DyG-RAG: Dynamic Graph Retrieval-Augmented Generation with Event-Centric Reasoning
Qingyun Sun, Jiaqi Yuan, Shan He +5
Graph Retrieval-Augmented Generation has emerged as a powerful paradigm for grounding large language models with external structured knowledge. However, existing Graph RAG methods…
Robust Graph Learning Against Adversarial Evasion Attacks via Prior-Free Diffusion-Based Structure Purification
Jiayi Luo, Qingyun Sun, Haonan Yuan +2
Adversarial evasion attacks pose significant threats to graph learning, with lines of studies that have improved the robustness of Graph Neural Networks (GNNs). However, existing w…
Discrete Curvature Graph Information Bottleneck
Xingcheng Fu, Jian Wang, Yisen Gao +4
Graph neural networks(GNNs) have been demonstrated to depend on whether the node effective information is sufficiently passing. Discrete curvature (Ricci curvature) is used to stud…
Graph Size-imbalanced Learning with Energy-guided Structural Smoothing
Jiawen Qin, Pengfeng Huang, Qingyun Sun +3
Graph is a prevalent data structure employed to represent the relationships between entities, frequently serving as a tool to depict and simulate numerous systems, such as molecule…
DG-Mamba: Robust and Efficient Dynamic Graph Structure Learning with Selective State Space Models
Haonan Yuan, Qingyun Sun, Zhaonan Wang +5
Dynamic graphs exhibit intertwined spatio-temporal evolutionary patterns, widely existing in the real world. Nevertheless, the structure incompleteness, noise, and redundancy resul…
GraphMoRE: Mitigating Topological Heterogeneity via Mixture of Riemannian Experts
Zihao Guo, Qingyun Sun, Haonan Yuan +4
Real-world graphs have inherently complex and diverse topological patterns, known as topological heterogeneity. Most existing works learn graph representation in a single constant…
GC-Bench: An Open and Unified Benchmark for Graph Condensation
Qingyun Sun, Ziying Chen, Beining Yang +6
Graph condensation (GC) has recently garnered considerable attention due to its ability to reduce large-scale graph datasets while preserving their essential properties. The core c…