30 citations · 77 across the 12 of their papers we have counts for
12 papers
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding
Zhongjian Zhang, Yue Yu, Mengmei Zhang +3
The remarkable success of large language models (LLMs) has motivated researchers to adapt them as universal predictors for various graph tasks. As a widely recognized paradigm, Gra…
Toward Graph-Tokenizing Large Language Models with Reconstructive Graph Instruction Tuning
Zhongjian Zhang, Xiao Wang, Mengmei Zhang +2
The remarkable success of large language models (LLMs) has motivated researchers to adapt them as universal predictors for various graph-related tasks, with the ultimate goal of de…
Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models
Zhibiao Wang, Yunlong Zhou, Ziwei Zhang +4
Graph Transformers, leveraging the global attention to capture long-range dependencies in graph structures, have significantly advanced graph machine learning, but face prohibitive…
Data-centric Federated Graph Learning with Large Language Models
Bo Yan, Zhongjian Zhang, Huabin Sun +3
In federated graph learning (FGL), a complete graph is divided into multiple subgraphs stored in each client due to privacy concerns, and all clients jointly train a global graph m…
Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective
Zhongjian Zhang, Mengmei Zhang, Xiao Wang +4
To preserve user privacy in recommender systems, federated recommendation (FR) based on federated learning (FL) emerges, keeping the personal data on the local client and updating…
Advancing Molecular Graph-Text Pre-training via Fine-grained Alignment
Yibo Li, Yuan Fang, Mengmei Zhang +1
Understanding molecular structure and related knowledge is crucial for scientific research. Recent studies integrate molecular graphs with their textual descriptions to enhance mol…
Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?
Zhongjian Zhang, Xiao Wang, Huichi Zhou +4
Graph neural networks (GNNs) are vulnerable to adversarial attacks, especially for topology perturbations, and many methods that improve the robustness of GNNs have received consid…
Minimum Topology Attacks for Graph Neural Networks
Mengmei Zhang, Xiao Wang, Chuan Shi +3
With the great popularity of Graph Neural Networks (GNNs), their robustness to adversarial topology attacks has received significant attention. Although many attack methods have be…
GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks
Mengmei Zhang, Mingwei Sun, Peng Wang +6
Large language models (LLMs) like ChatGPT, exhibit powerful zero-shot and instruction-following capabilities, have catalyzed a revolutionary transformation across diverse fields, e…
Endowing Pre-trained Graph Models with Provable Fairness
Zhongjian Zhang, Mengmei Zhang, Yue Yu +3
Pre-trained graph models (PGMs) aim to capture transferable inherent structural properties and apply them to different downstream tasks. Similar to pre-trained language models, PGM…
Graph Foundation Models: Concepts, Opportunities and Challenges
Jiawei Liu, Cheng Yang, Zhiyuan Lu +8
Foundation models have emerged as critical components in a variety of artificial intelligence applications, and showcase significant success in natural language processing and seve…
Provable Training for Graph Contrastive Learning
Yue Yu, Xiao Wang, Mengmei Zhang +2
Graph Contrastive Learning (GCL) has emerged as a popular training approach for learning node embeddings from augmented graphs without labels. Despite the key principle that maximi…