1 citations · 1 across the 9 of their papers we have counts for
10 papers
MMKG-R1: Constructing Multimodal Knowledge Graphs via Reinforcement Learning
Zongyu Wu, Yilong Wang, Xiaochen Wang +5
Multimodal knowledge graphs (MMKGs) organize information across different modalities to support multimodal Graph Retrieval-Augmented Generation (RAG), which aims to improve the per…
Multi-Granularity Context-Enhanced RAG over Multimodal Knowledge Graphs
Zongyu Wu, Yilong Wang, Xiaochen Wang +5
Retrieval-augmented generation (RAG) is widely used to mitigate hallucination issues in large language models (LLMs) and multimodal large language models (MLLMs). In particular, kn…
Trojaning the Alignment: Stealthy Backdoor Attacks against Graph Foundation Models
Minhua Lin, Zhicheng Gao, Yilong Wang +3
Graph Foundation Models (GFMs) on text-attributed graphs (TAGs) align graph representations with language semantics to support transferable graph learning. Despite these advantages…
Attack by Unlearning: Unlearning-Induced Adversarial Attacks on Graph Neural Networks
Jiahao Zhang, Yilong Wang, Suhang Wang
Graph neural networks (GNNs) are widely used for learning from graph-structured data in domains such as social networks, recommender systems, and financial platforms. To comply wit…
Query-Efficient Agentic Graph Extraction Attacks on GraphRAG Systems
Shuhua Yang, Jiahao Zhang, Yilong Wang +2
Graph-based retrieval-augmented generation (GraphRAG) systems construct knowledge graphs over document collections to support multi-hop reasoning. While prior work shows that Graph…
Unlearning Inversion Attacks for Graph Neural Networks
Jiahao Zhang, Yilong Wang, Zhiwei Zhang +2
Graph unlearning methods aim to efficiently remove the impact of sensitive data from trained GNNs without full retraining, assuming that deleted information cannot be recovered. In…
Bridging Source and Target Domains via Link Prediction for Unsupervised Domain Adaptation on Graphs
Yilong Wang, Tianxiang Zhao, Zongyu Wu +1
Graph neural networks (GNNs) have shown great ability for node classification on graphs. However, the success of GNNs relies on abundant labeled data, while obtaining high-quality…
Enhance GNNs with Reliable Confidence Estimation via Adversarial Calibration Learning
Yilong Wang, Jiahao Zhang, Tianxiang Zhao +1
Despite their impressive predictive performance, GNNs often exhibit poor confidence calibration, i.e., their predicted confidence scores do not accurately reflect true correctness…
LanP: Rethinking the Impact of Language Priors in Large Vision-Language Models
Zongyu Wu, Yuwei Niu, Hongcheng Gao +12
Large Vision-Language Models (LVLMs) have shown impressive performance in various tasks. However, LVLMs suffer from hallucination, which hinders their adoption in the real world. E…
Are You Using Reliable Graph Prompts? Trojan Prompt Attacks on Graph Neural Networks
Minhua Lin, Zhiwei Zhang, Enyan Dai +4
Graph Prompt Learning (GPL) has been introduced as a promising approach that uses prompts to adapt pre-trained GNN models to specific downstream tasks without requiring fine-tuning…