Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic
Graphs

Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs

18 May 2023

Ruofan Wu

Papers citing "Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs"

8 / 8 papers shown

Title
FedGrAINS: Personalized SubGraph Federated Learning with Adaptive Neighbor Sampling Emir Ceyani Han Xie Baturalp Buyukates Carl Yang Salman Avestimehr FedML 93 0 0 22 Jan 2025
Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence Liekang Zeng Shengyuan Ye Xu Chen Xiaoxi Zhang Ju Ren Jian Tang Yang Yang Xuemin Shen 52 2 0 08 Jan 2025
State Space Models on Temporal Graphs: A First-Principles Study Jintang Li Ruofan Wu Xinzhou Jin Boqun Ma Liang Chen Zibin Zheng 56 2 0 03 Jun 2024
A survey of dynamic graph neural networks Yanping Zheng Lu Yi Zhewei Wei AI4TS AI4CE 25 7 0 28 Apr 2024
TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation Learning Gangda Deng Hongkuan Zhou Hanqing Zeng Yinglong Xia Christopher Leung Jianbo Li Rajgopal Kannan Viktor Prasanna 11 1 0 08 Feb 2024
LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning Jintang Li Jiawang Dan Ruofan Wu Jing Zhou Sheng Tian ... Changhua Meng Weiqiang Wang Yuchang Zhu Liang Chen Zibin Zheng AI4CE 25 0 0 28 Nov 2023
A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks Jintang Li Huizhe Zhang Ruofan Wu Zulun Zhu Baokun Wang Changhua Meng Zibin Zheng Liang Chen SSL 15 4 0 30 May 2023
What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders Jintang Li Ruofan Wu Wangbin Sun Liang Chen Sheng Tian Liang Zhu Changhua Meng Zibin Zheng Weiqiang Wang SSL 9 77 0 20 May 2022