Accurate, Efficient and Scalable Graph Embedding

28 October 2018

Papers citing "Accurate, Efficient and Scalable Graph Embedding"

23 / 23 papers shown

Title
Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks Xuewen Dong Jiachen Li Shujun Li Zhichao You Qiang Qu Yaroslav Kholodov Yulong Shen AAML 48 0 0 12 Mar 2025
BatchGNN: Efficient CPU-Based Distributed GNN Training on Very Large Graphs Loc Hoang Rita Brugarolas Brufau Ke Ding Bo Wu GNN 35 2 0 23 Jun 2023
Rethinking Efficiency and Redundancy in Training Large-scale Graphs Xin Liu Xunbin Xiong Yurui Lai Runzhen Xue Shirui Pan Xiaochun Ye Xiaochun Ye 23 1 0 02 Sep 2022
Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks Chuang Liu Xueqi Ma Yinbing Zhan Liang Ding Dapeng Tao Bo Du Wenbin Hu Danilo Mandic 44 29 0 18 Jul 2022
Scalable algorithms for physics-informed neural and graph networks K. Shukla Mengjia Xu N. Trask George Karniadakis PINN AI4CE 75 40 0 16 May 2022
Trustworthy Graph Neural Networks: Aspects, Methods and Trends He Zhang Bang Wu Xingliang Yuan Shirui Pan Hanghang Tong Jian Pei 47 104 0 16 May 2022
Survey on Graph Neural Network Acceleration: An Algorithmic Perspective Xin Liu Yurui Lai Lei Deng Guoqi Li Xiaochun Ye Xiaochun Ye Shirui Pan Yuan Xie GNN 16 42 0 10 Feb 2022
Decoupling the Depth and Scope of Graph Neural Networks Hanqing Zeng Muhan Zhang Yinglong Xia Ajitesh Srivastava Andrey Malevich Rajgopal Kannan Viktor Prasanna Long Jin Ren Chen GNN 33 144 0 19 Jan 2022
GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design Sung Une Lee Boming Xia Yongan Zhang Ang Li Yingyan Lin GNN 60 48 0 22 Dec 2021
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods Derek Lim Felix Hohne Xiuyu Li Sijia Huang Vaishnavi Gupta Omkar Bhalerao Ser-Nam Lim 61 340 0 27 Oct 2021
G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency Yongan Zhang Haoran You Yonggan Fu Tong Geng Ang Li Yingyan Lin GNN 23 28 0 18 Sep 2021
GNNSampler: Bridging the Gap between Sampling Algorithms of GNN and Hardware Xin Liu Yurui Lai Shuhan Song Zhengyang Lv Wenming Li Guangyu Sun Xiaochun Ye Xiaochun Ye 24 13 0 26 Aug 2021
A High Throughput Parallel Hash Table on FPGA using XOR-based Memory Ruizhi Zhang Sasindu Wijeratne Yang Yang S. Kuppannagari Viktor Prasanna 22 5 0 07 Aug 2021
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks Zhiqian Chen Fanglan Chen Lei Zhang Taoran Ji Kaiqun Fu Liang Zhao Feng Chen Lingfei Wu Charu C. Aggarwal Chang-Tien Lu 53 18 0 21 Jul 2021
Sampling methods for efficient training of graph convolutional networks: A survey Xin Liu Yurui Lai Lei Deng Guoqi Li Xiaochun Ye Xiaochun Ye GNN 29 100 0 10 Mar 2021
Early-Bird GCNs: Graph-Network Co-Optimization Towards More Efficient GCN Training and Inference via Drawing Early-Bird Lottery Tickets Haoran You Zhihan Lu Zijian Zhou Y. Fu Yingyan Lin GNN 41 30 0 01 Mar 2021
Accurate, Efficient and Scalable Training of Graph Neural Networks Hanqing Zeng Hongkuan Zhou Ajitesh Srivastava Rajgopal Kannan Viktor Prasanna GNN 14 8 0 05 Oct 2020
GraphCrop: Subgraph Cropping for Graph Classification Yiwei Wang Wei Wang Keli Zhang Yujun Cai Bryan Hooi 25 57 0 22 Sep 2020
C-SAW: A Framework for Graph Sampling and Random Walk on GPUs Santosh Pandey Lingda Li A. Hoisie Xin Li Hang Liu 30 60 0 18 Sep 2020
GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms Hanqing Zeng Viktor Prasanna GNN 22 126 0 31 Dec 2019
GraphSAINT: Graph Sampling Based Inductive Learning Method Hanqing Zeng Hongkuan Zhou Ajitesh Srivastava Rajgopal Kannan Viktor Prasanna GNN 81 954 0 10 Jul 2019
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 310 2,896 0 15 Sep 2016
Benefits of depth in neural networks Matus Telgarsky 179 604 0 14 Feb 2016