15 citations · 63 across the 16 of their papers we have counts for
11 papers
OFL-W3: A One-shot Federated Learning System on Web 3.0
Linshan Jiang, Moming Duan, Bingsheng He +4
Federated Learning (FL) addresses the challenges posed by data silos, which arise from privacy, security regulations, and ownership concerns. Despite these barriers, FL enables the…
FlowWalker: A Memory-efficient and High-performance GPU-based Dynamic Graph Random Walk Framework
Junyi Mei, Shixuan Sun, Chao Li +9
Dynamic graph random walk (DGRW) emerges as a practical tool for capturing structural relations within a graph. Effectively executing DGRW on GPU presents certain challenges. First…
Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation
Zhen Zhang, Meihan Liu, Anhui Wang +4
Unsupervised Graph Domain Adaptation (UGDA) has emerged as a practical solution to transfer knowledge from a label-rich source graph to a completely unlabelled target graph. Howeve…
BuffGraph: Enhancing Class-Imbalanced Node Classification via Buffer Nodes
Qian Wang, Zemin Liu, Zhen Zhang +1
Class imbalance in graph-structured data, where minor classes are significantly underrepresented, poses a critical challenge for Graph Neural Networks (GNNs). To address this chall…
Live Graph Lab: Towards Open, Dynamic and Real Transaction Graphs with NFT
Zhen Zhang, Bingqiao Luo, Shengliang Lu +1
Numerous studies have been conducted to investigate the properties of large-scale temporal graphs. Despite the ubiquity of these graphs in real-world scenarios, it's usually imprac…
FusionAI: Decentralized Training and Deploying LLMs with Massive Consumer-Level GPUs
Zhenheng Tang, Yuxin Wang, Xin He +8
The rapid growth of memory and computation requirements of large language models (LLMs) has outpaced the development of hardware, hindering people who lack large-scale high-end GPU…