Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions
In this survey, we dive into Tabular Data Learning (TDL) using Graph Neural Networks
(GNNs), a domain where deep learning-based approaches have increasingly shown …
(GNNs), a domain where deep learning-based approaches have increasingly shown …
HLGST: Hybrid local–global spatio-temporal model for travel time estimation using Siamese graph convolutional with triplet networks
Travel time estimation (TTE) is a crucial and challenging task due to the complex spatial and
dynamic temporal correlations between local and global traffic regions. Though many …
dynamic temporal correlations between local and global traffic regions. Though many …
Improved similarity assessment and spectral clustering for unsupervised linking of data extracted from bridge inspection reports
K Liu, N El-Gohary - Advanced Engineering Informatics, 2022 - Elsevier
Textual bridge inspection reports are important data sources for supporting data-driven
bridge deterioration prediction and maintenance decision making. Information extraction …
bridge deterioration prediction and maintenance decision making. Information extraction …
TabGraphs: A Benchmark and Strong Baselines for Learning on Graphs with Tabular Node Features
Tabular machine learning is an important field for industry and science. In this field, table
rows are usually treated as independent data samples, but additional information about …
rows are usually treated as independent data samples, but additional information about …
Business entity matching with siamese graph convolutional networks
Data integration has been studied extensively for decades and approached from different
angles. However, this domain still remains largely rule-driven and lacks universal …
angles. However, this domain still remains largely rule-driven and lacks universal …
Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning
M Atzeni - 2024 - infoscience.epfl.ch
The ability to reason, plan and solve highly abstract problems is a hallmark of human
intelligence. Recent advancements in artificial intelligence, propelled by deep neural …
intelligence. Recent advancements in artificial intelligence, propelled by deep neural …
Clustered Federated Learning for Heterogeneous Feature Spaces using Siamese Graph Convolutional Neural Network Distance Prediction
Federated learning (FL) has been proposed to enhance performance of local machine
learning models across multiple devices while maintaining data privacy. One of the main …
learning models across multiple devices while maintaining data privacy. One of the main …
Mobile Network Configuration Recommendation using Deep Generative Graph Neural Network
There are vast number of configurable parameters in a Radio Access Telecom Network. A
significant amount of these parameters is configured by Radio Node or cell based on their …
significant amount of these parameters is configured by Radio Node or cell based on their …
Building knowledge graphs from technical documents using named entity recognition and edge weight updating neural network with triplet loss for entity normalization
SH Jeon, HJ Lee, J Park, S Cho - Intelligent Data Analysis, 2024 - content.iospress.com
Attempts to express information from various documents in graph form are rapidly
increasing. The speed and volume in which these documents are being generated call for …
increasing. The speed and volume in which these documents are being generated call for …
Named Entity Normalization Model Using Edge Weight Updating Neural Network: Assimilation Between Knowledge-Driven Graph and Data-Driven Graph
SH Jeon, S Cho - arxiv preprint arxiv:2106.07549, 2021 - arxiv.org
Discriminating the matched named entity pairs or identifying the entities' canonical forms are
critical in text mining tasks. More precise named entity normalization in text mining will …
critical in text mining tasks. More precise named entity normalization in text mining will …