A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations

19 January 2021

Papers citing "A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations"

7 / 7 papers shown

Title
Nonlinear Correct and Smooth for Semi-Supervised Learning Yuanhang Shao Xiuwen Liu 26 1 0 09 Oct 2023
LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation Rui Xue Haoyu Han MohamadAli Torkamani Jian Pei Xiaorui Liu GNN 31 21 0 03 Feb 2023
How Powerful is Implicit Denoising in Graph Neural Networks Songtao Liu Rex Ying Hanze Dong Lu Lin Jinghui Chen Di Wu GNN AI4CE 22 3 0 29 Sep 2022
Graph-Based Methods for Discrete Choice K. Tomlinson Austin R. Benson 13 3 0 23 May 2022
Why Propagate Alone? Parallel Use of Labels and Features on Graphs Yangkun Wang Jiarui Jin Weinan Zhang Yongyi Yang Jiuhai Chen Quan Gan Yong Yu Zheng-Wei Zhang Zengfeng Huang David Wipf AAML 58 12 0 14 Oct 2021
Multi-scale Attributed Node Embedding Benedek Rozemberczki Carl Allen Rik Sarkar GNN 148 837 0 28 Sep 2019
Found Graph Data and Planted Vertex Covers Austin R. Benson Jon M. Kleinberg GNN 31 13 0 03 May 2018