Memory attention enhanced graph convolution long short‐term memory network for traffic forecasting
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22855
Abstract: In recent years, traffic forecasting has gradually attracted attention in data mining because of the increasing availability of large‐scale traffic data. However, it faces substantial challenges of complex temporal‐spatial correlations in traffic. Recent studies mainly… read more here.
Keywords: traffic forecasting; attention; traffic; graph convolution ... See more keywords
Comprehensive Exploration of Target‐specific Ligands Using a Graph Convolution Neural Network
Sign Up to like & getrecommendations! Published in 2019 at "Molecular Informatics"
DOI: 10.1002/minf.201900095
Abstract: Machine learning approaches are widely used to evaluate ligand activities of chemical compounds toward potential target proteins. Especially, exploration of highly selective ligands is important for the development of new drugs with higher safety. One… read more here.
Keywords: convolution neural; graph convolution; exploration; target ... See more keywords
Near-surface PM2.5 prediction combining the complex network characterization and graph convolution neural network
Sign Up to like & getrecommendations! Published in 2021 at "Neural Computing and Applications"
DOI: 10.1007/s00521-021-06300-3
Abstract: Massive studies focus on the prediction of main pollutants, to improve air quality by revealing the evolution of pollutants. However, existing prediction methods mostly emphasize the fitting analysis of time series, but ignore the spatial… read more here.
Keywords: convolution neural; network; graph convolution; neural network ... See more keywords
Predicting miRNA-disease associations based on lncRNA-miRNA interactions and graph convolution networks
Sign Up to like & getrecommendations! Published in 2022 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbac495
Abstract: Increasing studies have proved that microRNAs (miRNAs) are critical biomarkers in the development of human complex diseases. Identifying disease-related miRNAs is beneficial to disease prevention, diagnosis and remedy. Based on the assumption that similar miRNAs… read more here.
Keywords: mirna interactions; disease; disease associations; lncrna mirna ... See more keywords
Structure-Aware Multi-Hop Graph Convolution for Graph Neural Networks
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3149619
Abstract: We present a spatial graph convolution (GC) to classify signals on a graph. Existing GC methods are limited in using the structural information in the feature space. Furthermore, GCs only aggregate features from one-hop neighboring… read more here.
Keywords: multi hop; neural networks; graph convolution; graph neural ... See more keywords
Spatial-Temporal Aggregation Graph Convolution Network for Efficient Mobile Cellular Traffic Prediction
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Communications Letters"
DOI: 10.1109/lcomm.2021.3138075
Abstract: Accurate cellular traffic prediction is challenging due to the complex spatial topology of cellular network and the dynamic temporal feature of mobile traffic. To overcome these problems, this letter proposes a spatial-temporal aggregation graph convolution… read more here.
Keywords: spatial temporal; traffic; convolution network; graph convolution ... See more keywords
Remote Sensing Image Classification Based on a Cross-Attention Mechanism and Graph Convolution
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2020.3026587
Abstract: An attention mechanism assigns different weights to different features to help a model select the features most valuable for accurate classification. However, the traditional attention mechanism algorithm often allocates weights in a one-way fashion, which… read more here.
Keywords: attention; classification; graph convolution; attention mechanism ... See more keywords
Attention-Driven Graph Convolution Network for Remote Sensing Image Retrieval
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2021.3105448
Abstract: Graph convolution networks (GCNs) are useful in remote sensing (RS) image retrieval. It is found to be effective because, in a graph representation, the relative geometrical interactions between different regions (or segments) are appropriately captured,… read more here.
Keywords: remote sensing; graph convolution; image retrieval; attention ... See more keywords
Multiscale 3D-Shift Graph Convolution Network for Emotion Recognition From Human Actions
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Intelligent Systems"
DOI: 10.1109/mis.2022.3147585
Abstract: Emotion recognition from body gestures is challenging since similar emotions can be expressed by arbitrary spatial configurations of joints, which results in relying on modeling spatial-temporal patterns from a more global level. However, most recent… read more here.
Keywords: recognition; emotion recognition; graph convolution;
An Optimized Temporal-Spatial Gated Graph Convolution Network for Traffic Forecasting
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Intelligent Transportation Systems Magazine"
DOI: 10.1109/mits.2019.2962138
Abstract: Traffic forecasting is a challenging problem because of the irregular and complex road network in space and the dynamic and non-stationary traffic flow in time. To solve this problem, the recently proposed temporal graph convolution… read more here.
Keywords: traffic; convolution network; graph convolution; network ... See more keywords
DGCddG: Deep Graph Convolution for Predicting Protein-Protein Binding Affinity Changes Upon Mutations.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE/ACM transactions on computational biology and bioinformatics"
DOI: 10.1109/tcbb.2022.3233627
Abstract: Effectively and accurately predicting the effects of interactions between proteins after amino acid mutations is a key issue for understanding the mechanism of protein function and drug design. In this study, we present a deep… read more here.
Keywords: graph convolution; binding affinity; protein;