Missing Data Estimation in Temporal Multilayer Position-Aware Graph Neural Network (TMP-GNN)

Najafi Bahareh; Parsaeefard Saeedeh; Leon-Garcia Alberto

首页> 外文期刊>Machine Learning and Knowledge Extraction >Missing Data Estimation in Temporal Multilayer Position-Aware Graph Neural Network (TMP-GNN)

【24h】

Missing Data Estimation in Temporal Multilayer Position-Aware Graph Neural Network (TMP-GNN)

机译：

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

GNNs have been proven to perform highly effectively in various node-level, edge-level, and graph-level prediction tasks in several domains. Existing approaches mainly focus on static graphs. However, many graphs change over time and their edge may disappear, or the node/edge attribute may alter from one time to the other. It is essential to consider such evolution in the representation learning of nodes in time-varying graphs. In this paper, we propose a Temporal Multilayer Position-Aware Graph Neural Network (TMP-GNN), a node embedding approach for dynamic graphs that incorporates the interdependence of temporal relations into embedding computation. We evaluate the performance of TMP-GNN on two different representations of temporal multilayered graphs. The performance is assessed against the most popular GNNs on a node-level prediction task. Then, we incorporate TMP-GNN into a deep learning framework to estimate missing data and compare the performance with their corresponding competent GNNs from our former experiment, and a baseline method. Experimental results on four real-world datasets yield up to 58 lower ROC AUC for the pair-wise node classification task, and 96 lower MAE in missing feature estimation, particularly for graphs with a relatively high number of nodes and lower mean degree of connectivity.

著录项

来源
《Machine Learning and Knowledge Extraction》 |2022年第2期|397-417|共21页
作者
Najafi Bahareh; Parsaeefard Saeedeh; Leon-Garcia Alberto;
展开▼
作者单位

University of Toronto The Edward S Rogers Sr Department of Electrical & Computer Engineering;

展开▼
收录信息
原文格式 PDF
正文语种英语
中图分类
关键词
node embedding; Position-Aware Graph Neural Network; spatio-temporal measurements; temporal multilayer graph; missing data analysis;

相似文献

外文文献
中文文献

1. Data from University of Toronto Advance Knowledge in Knowledge Extraction Missing Data Estimation in Temporal Multilayer Position-Aware Graph Neural Network (TMP-GNN) [J] . Network Daily News . 2022,第7期

机译：Data from University of Toronto Advance Knowledge in Knowledge Extraction Missing Data Estimation in Temporal Multilayer Position-Aware Graph Neural Network (TMP-GNN)
2. Findings from University of Houston Provides New Data about Networks (Missing Pavement Performance Data Imputation Using Graph Neural Networks) [J] . Network Daily News . 2022,第4期

机译：Findings from University of Houston Provides New Data about Networks (Missing Pavement Performance Data Imputation Using Graph Neural Networks)
3. Reports from Zhejiang University of Technology Add New Data to Findings in Networks (Bidirectional Spatial-temporal Traffic Data Imputation Via Graph Attention Recurrent Neural Network) [J] . Network Daily News . 2023,第1期

机译：Reports from Zhejiang University of Technology Add New Data to Findings in Networks (Bidirectional Spatial-temporal Traffic Data Imputation Via Graph Attention Recurrent Neural Network)
4. An Improved DINEOF Algorithm for Filling Missing Values in Spatio-Temporal Sea Surface Temperature Data [O] . Ping B., Su, F. Z., Meng, Y. S. 2016

机译：an Improved DINEOF algorithm for Filling missing Values in spatio-Temporal sea surface Temperature Data

Missing Data Estimation in Temporal Multilayer Position-Aware Graph Neural Network (TMP-GNN)

摘要

著录项

相似文献

相关主题

期刊订阅