Graph Convolutional Networks for Graphs Containing Missing Features

9 July 2020

Papers citing "Graph Convolutional Networks for Graphs Containing Missing Features"

8 / 8 papers shown

Title
Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks Federico Errica Mathias Niepert TPM 25 4 0 17 May 2023
Data Augmentation for Deep Graph Learning: A Survey Kaize Ding Zhe Xu Hanghang Tong Huan Liu OOD GNN 27 218 0 16 Feb 2022
FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction Liang Peng Nan Wang Nicha Dvornek Xiaofeng Zhu Xiaoxiao Li FedML AI4CE 22 70 0 19 Dec 2021
On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features Emanuele Rossi Henry Kenlay Maria I. Gorinova B. Chamberlain Xiaowen Dong M. Bronstein 28 87 0 23 Nov 2021
On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs Hejie Cui Zijie Lu Pan Li Carl Yang 15 80 0 03 Jul 2021
A Survey on Knowledge Graphs: Representation, Acquisition and Applications Shaoxiong Ji Shirui Pan Erik Cambria Pekka Marttinen Philip S. Yu 181 1,940 0 02 Feb 2020
Recurrent Neural Networks for Multivariate Time Series with Missing Values Zhengping Che S. Purushotham Kyunghyun Cho David Sontag Yan Liu AI4TS 213 1,897 0 06 Jun 2016
MissForest - nonparametric missing value imputation for mixed-type data D. Stekhoven Peter Buhlmann 177 4,222 0 04 May 2011