4 citations · 17 across the 13 of their papers we have counts for
13 papers
UniErase: Towards Balanced and Precise Unlearning in Language Models
Miao Yu, Liang Lin, Guibin Zhang +7
Large language models (LLMs) require iterative updates to address the outdated information problem, where LLM unlearning offers an approach for selective removal. However, mainstre…
Mind Scramble: Unveiling Large Language Model Psychology Via Typoglycemia
Miao Yu, Junyuan Mao, Guibin Zhang +7
Research into the external behaviors and internal mechanisms of large language models (LLMs) has shown promise in addressing complex tasks in the physical world. Studies suggest th…
NetSafe: Exploring the Topological Safety of Multi-agent Networks
Miao Yu, Shilong Wang, Guibin Zhang +6
Large language models (LLMs) have empowered nodes within multi-agent networks with intelligence, showing growing applications in both academia and industry. However, how to prevent…
G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks
Guibin Zhang, Yanwei Yue, Xiangguo Sun +6
Recent advancements in large language model (LLM)-based agents have demonstrated that collective intelligence can significantly surpass the capabilities of individual agents, prima…
The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs
Kun Wang, Guibin Zhang, Xinnan Zhang +6
Graph Neural Networks (GNNs) have become pivotal tools for a range of graph-based learning tasks. Notably, most current GNN architectures operate under the assumption of homophily,…
All Nodes are created Not Equal: Node-Specific Layer Aggregation and Filtration for GNN
Shilong Wang, Hao Wu, Yifan Duan +6
The ever-designed Graph Neural Networks, though opening a promising path for the modeling of the graph-structure data, unfortunately introduce two daunting obstacles to their deplo…
Graph Sparsification via Mixture of Graphs
Guibin Zhang, Xiangguo Sun, Yanwei Yue +4
Graph Neural Networks (GNNs) have demonstrated superior performance across various graph learning tasks but face significant computational challenges when applied to large-scale gr…
DynST: Dynamic Sparse Training for Resource-Constrained Spatio-Temporal Forecasting
Hao Wu, Haomin Wen, Guibin Zhang +5
The ever-increasing sensor service, though opening a precious path and providing a deluge of earth system data for deep-learning-oriented earth science, sadly introduce a daunting…
Modeling Spatio-temporal Dynamical Systems with Neural Discrete Learning and Levels-of-Experts
Kun Wang, Hao Wu, Guibin Zhang +5
In this paper, we address the issue of modeling and estimating changes in the state of the spatio-temporal dynamical systems based on a sequence of observations like video frames.…
EXGC: Bridging Efficiency and Explainability in Graph Condensation
Junfeng Fang, Xinglin Li, Yongduo Sui +5
Graph representation learning on vast datasets, like web data, has made significant strides. However, the associated computational and storage overheads raise concerns. In sight of…
Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness
Guibin Zhang, Yanwei Yue, Kun Wang +7
Graph Neural Networks (GNNs) excel in various graph learning tasks but face computational challenges when applied to large-scale graphs. A promising solution is to remove non-essen…
CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks
Yifan Duan, Guibin Zhang, Shilong Wang +6
Credit card fraud poses a significant threat to the economy. While Graph Neural Network (GNN)-based fraud detection methods perform well, they often overlook the causal effect of a…
The Snowflake Hypothesis: Training Deep GNN with One Node One Receptive field
Kun Wang, Guohao Li, Shilong Wang +6
Despite Graph Neural Networks demonstrating considerable promise in graph representation learning tasks, GNNs predominantly face significant issues with over-fitting and over-smoot…