NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning

17 June 2022

Papers citing "NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning"

9 / 9 papers shown

Title
Toward Data-centric Directed Graph Learning: An Entropy-driven Approach Xunkai Li Zhengyu Wu Kaichi Yu Hongchao Qin Guang Zeng Rong-Hua Li Guoren Wang 27 0 0 02 May 2025
Graph Condensation: A Survey Xin Gao Junliang Yu Wei Jiang Tong Chen Wentao Zhang Hongzhi Yin DD 83 19 0 28 Jan 2025
Molecular Topological Profile (MOLTOP) -- Simple and Strong Baseline for Molecular Graph Classification Jakub Adamczyk Wojciech Czech 27 1 0 16 Jul 2024
Acceleration Algorithms in GNNs: A Survey Lu Ma Zeang Sheng Xunkai Li Xin Gao Zhezheng Hao Ling Yang Wentao Zhang Bin Cui GNN 34 3 0 07 May 2024
Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagation Xin Gao Wentao Zhang Junliang Yu Yingxiao Shao Quoc Viet Hung Nguyen Bin Cui Hongzhi Yin AI4CE GNN 22 8 0 17 Oct 2023
Structure-Aware Group Discrimination with Adaptive-View Graph Encoder: A Fast Graph Contrastive Learning Framework Zhenshuo Zhang Yun Zhu Haizhou Shi Siliang Tang 14 1 0 09 Mar 2023
Rethinking the positive role of cluster structure in complex networks for link prediction tasks Shan-Shan Zhang Wenjiao Zhang Zhan Bu 14 0 0 04 Nov 2022
Efficient Graph Neural Network Inference at Large Scale Xin-pu Gao Wentao Zhang Yingxia Shao Quoc Viet Hung Nguyen Bin Cui Hongzhi Yin AI4CE GNN 39 4 0 01 Nov 2022
Representation Learning on Graphs with Jumping Knowledge Networks Keyulu Xu Chengtao Li Yonglong Tian Tomohiro Sonobe Ken-ichi Kawarabayashi Stefanie Jegelka GNN 226 1,726 0 09 Jun 2018