Results 21 to 30 of about 22,751 (263)
Frontiers in Genetics, 2023 Spatially resolved transcriptomics (SRT) provides an unprecedented opportunity to investigate the complex and heterogeneous tissue organization. However, it is challenging for a single model to learn an effective representation within and across spatial ...
Xinxing Li +4 more
doaj +1 more source
SARW: Similarity-Aware Random Walk for GCN
, 2023 Graph Convolutional Network (GCN) is an important method for learning graph representations of nodes. For large-scale graphs, the GCN could meet with the neighborhood expansion phenomenon, which makes the model complexity high and the training time long.
Wu, Ou +6 more
core +1 more source
GCN Global Communications Newsletter [PDF]
IEEE Communications Magazine, 2020 With this issue we begin a new series of eight interviews with the Officers of the IEEE ComSoc Member and Global Activities (MGA) Council, which will be published every month in the Global Communications Newsletter.
Stefano Bregni +3 more
openaire +2 more sources
The Study of Taiwanese Students' Experiences with Geometric Calculation with Number (GCN) and Their Performance on GCN and Geometric Proof (GP). [PDF]
, 2010 In Taiwan, students have considerable experience with tasks requiring geometric calculations with number (GCN) prior to their study of geometric proof (GP).
Hsu, Hui Yu (Angela)
core +7 more sources
Atoms, 2023 In this work, a group-11 metal nanoparticle-embedded, graphitic carbon nitride-based, resistive-type sensor was developed for room temperature acetone sensing.
Nihal +5 more
doaj +1 more source
IET Image Processing, 2022 Skeleton‐based action recognition is an inspired yet challenging task in computer vision. Recently, the latest graph convolutional network (GCN), which generalises well‐established convolutional neural networks to non‐Euclidean structures, is proven to ...
Jun Tang +4 more
doaj +1 more source
Quadratic GCN for Graph Classification
CoRR, 2021 Graph Convolutional Networks (GCNs) have been extensively used to classify vertices in graphs and have been shown to outperform other vertex classification methods. GCNs have been extended to graph classification tasks (GCT). In GCT, graphs with different numbers of edges and vertices belong to different classes, and one attempts to predict the graph ...
Omer Nagar +3 more
openaire +3 more sources
, 2022 iPiDA-GCN mainly contains three modules: (i) Heterogeneous network construction (Fig 1A). Three kinds of edges are collected in the heterogeneous piRNA-disease association network, including piRNA-piRNA similarities, disease-disease similarities and ...
Bin Liu (5899) +2 more
core +1 more source
The impact of GCN layers on the predictive performance of iPiDA-GCN on .
, 2022 The impact of GCN layers on the predictive performance of iPiDA-GCN on .
Bin Liu (5899) +2 more
core +1 more source
Analyzing GCN Aggregation on GPU
IEEE Access, 2022 Graph convolutional neural networks (GCNs) are emerging neural networks for graph structures that include large features associated with each vertex. The operations of GCN can be divided into two phases - aggregation and combination. While the combination just performs matrix multiplications using trained weights and aggregated features, the ...
Inje Kim +4 more
openaire +2 more sources