2 citations · 3 across the 34 of their papers we have counts for
23 papers · 1 filter
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…
Unlocking the Potentials of Retrieval-Augmented Generation for Diffusion Language Models
Chuanyue Yu, Jiahui Wang, Yuhan Li +6
Diffusion Language Models (DLMs) have recently demonstrated remarkable capabilities in natural language processing tasks. However, the potential of Retrieval-Augmented Generation (…
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,…
SAGFM: Enhancing Robust Graph Foundation Models with Structure-Aware Semantic Augmentation
Junhua Shi, Qingyun Sun, Haonan Yuan +1
We present Graph Foundation Models (GFMs) which have made significant progress in various tasks, but their robustness against domain noise, structural perturbations, and adversaria…
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…
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…
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…
Mitigating Message Imbalance in Fraud Detection with Dual-View Graph Representation Learning
Yudan Song, Yuecen Wei, Yuhang Lu +6
Graph representation learning has become a mainstream method for fraud detection due to its strong expressive power, which focuses on enhancing node representations through improve…
An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks
Jinyan Wang, Liu Yang, Yuecen Wei +5
Graph Neural Network-based methods face privacy leakage risks due to the introduction of topological structures about the targets, which allows attackers to bypass the target's pri…
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…
Bi-Directional Multi-Scale Graph Dataset Condensation via Information Bottleneck
Xingcheng Fu, Yisen Gao, Beining Yang +4
Dataset condensation has significantly improved model training efficiency, but its application on devices with different computing power brings new requirements for different data…
Prompt-based Unifying Inference Attack on Graph Neural Networks
Yuecen Wei, Xingcheng Fu, Lingyun Liu +3
Graph neural networks (GNNs) provide important prospective insights in applications such as social behavior analysis and financial risk analysis based on their powerful learning ca…
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…
Scaling Law Hypothesis for Multimodal Model
Qingyun Sun, Zhen Guo, PIN AI Team
We propose a scaling law hypothesis for multimodal models processing text, audio, images, and video within a shared token and embedding space. Our framework predicts model performa…
Motif-driven Subgraph Structure Learning for Graph Classification
Zhiyao Zhou, Sheng Zhou, Bochao Mao +5
To mitigate the suboptimal nature of graph structure, Graph Structure Learning (GSL) has emerged as a promising approach to improve graph structure and boost performance in downstr…
IGL-Bench: Establishing the Comprehensive Benchmark for Imbalanced Graph Learning
Jiawen Qin, Haonan Yuan, Qingyun Sun +8
Deep graph learning has gained grand popularity over the past years due to its versatility and success in representing graph data across a wide range of domains. However, the perva…
Hyperbolic Geometric Latent Diffusion Model for Graph Generation
Xingcheng Fu, Yisen Gao, Yuecen Wei +4
Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation…