Lasagne: A multi-layer graph convolutional network framework via node-aware deep architecture
Graph convolutional networks (GCNs) have been successfully applied in many different real-
world tasks. However, most of the existing methods are based on shallow GCN, because …
world tasks. However, most of the existing methods are based on shallow GCN, because …
KLGCN: Knowledge graph-aware Light Graph Convolutional Network for recommender systems
F Wang, Y Li, Y Zhang, D Wei - Expert Systems with Applications, 2022 - Elsevier
Most popular recommender systems learn the embedding of users and items through
capturing valuable information from user–item interactions or item knowledge graph (KG) …
capturing valuable information from user–item interactions or item knowledge graph (KG) …
On filter size in graph convolutional networks
Recently, many researchers have been focusing on the definition of neural networks for
graphs. The basic component for many of these approaches remains the graph convolution …
graphs. The basic component for many of these approaches remains the graph convolution …
Multiedge graph convolutional network for house price prediction
Accurate house price prediction allows construction investors to make informed decisions
about the housing market and understand the growth opportunities for development and the …
about the housing market and understand the growth opportunities for development and the …
One size fits all: A unified traffic predictor for capturing the essential spatial–temporal dependency
Traffic prediction is a keystone for building smart cities in the new era and has found wide
applications in traffic scheduling and management, environment policy making, public …
applications in traffic scheduling and management, environment policy making, public …
DFinder: a novel end-to-end graph embedding-based method to identify drug–food interactions
Abstract Motivation Drug–food interactions (DFIs) occur when some constituents of food
affect the bioaccessibility or efficacy of the drug by involving in drug pharmacodynamic …
affect the bioaccessibility or efficacy of the drug by involving in drug pharmacodynamic …
The heterophilic snowflake hypothesis: Training and empowering gnns for heterophilic graphs
Graph Neural Networks (GNNs) have become pivotal tools for a range of graph-based
learning tasks. Notably, most current GNN architectures operate under the assumption of …
learning tasks. Notably, most current GNN architectures operate under the assumption of …
[HTML][HTML] Construction safety risk model with construction accident network: A graph convolutional network approach
Construction risk assessment (RA) based on expert knowledge and experience incorporates
uncertainties that reduce its accuracy and effectiveness in implementing countermeasures …
uncertainties that reduce its accuracy and effectiveness in implementing countermeasures …
Alg: Fast and accurate active learning framework for graph convolutional networks
Graph Convolutional Networks (GCNs) have become state-of-the-art methods in many
supervised and semi-supervised graph representation learning scenarios. In order to …
supervised and semi-supervised graph representation learning scenarios. In order to …
Perturbation-augmented graph convolutional networks: A graph contrastive learning architecture for effective node classification tasks
Q Guo, X Yang, F Zhang, T Xu - Engineering Applications of Artificial …, 2024 - Elsevier
In the context of recent advances in Graph Convolutional Networks (GCNs) for semi-
supervised learning, a significant highlight is the potential of Graph Contrastive Learning …
supervised learning, a significant highlight is the potential of Graph Contrastive Learning …