• FastGCN

  • Referenced in 7 articles [sw38089]
  • FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. The graph convolutional networks ... graphs. To relax the requirement of simultaneous availability of test data, we interpret graph convolutions...
  • DropEdge

  • Referenced in 3 articles [sw37753]
  • DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. Over-fitting and over-smoothing ... main obstacles of developing deep Graph Convolutional Networks (GCNs) for node classification. In particular, over ... input features with the increase in network depth. This paper proposes DropEdge, a novel ... certain number of edges from the input graph at each training epoch, acting like...
  • LightGCN

  • Referenced in 2 articles [sw37571]
  • LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. Graph Convolution Network (GCN) has become ... which is originally designed for graph classification tasks and equipped with many neural network operations...
  • CayleyNets

  • Referenced in 5 articles [sw38090]
  • CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters. The rise of graph-structured ... social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success ... spectral domain convolutional architecture for deep learning on graphs. The core ingredient of our model...
  • DeepGCNs

  • Referenced in 2 articles [sw38086]
  • GCNs Go as Deep as CNNs? Convolutional Neural Networks (CNNs) achieve impressive performance ... Euclidean data. To overcome this challenge, Graph Convolutional Networks (GCNs) build graphs to represent ... from CNNs, specifically residual/dense connections and dilated convolutions, and adapting them to GCN architectures. Extensive...
  • Pixel2Mesh

  • Referenced in 4 articles [sw31205]
  • represents 3D mesh in a graph-based convolutional neural network and produces correct geometry...
  • CGC-Net

  • Referenced in 1 article [sw39633]
  • Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images. Colorectal cancer (CRC) grading ... present a novel cell-graph convolutional neural network (CGC-Net) that converts each large histology ... introduce Adaptive GraphSage, which is a graph convolution technique that combines multi-level features...
  • GraphSAINT

  • Referenced in 1 article [sw37758]
  • Graph Sampling Based Inductive Learning Method. Graph Convolutional Networks (GCNs) are powerful models for learning...
  • paper2repo

  • Referenced in 1 article [sw32544]
  • paper2repo integrates text encoding and constrained graph convolutional networks (GCN) to automatically learn...
  • LightTrack

  • Referenced in 1 article [sw39261]
  • tracking. We also propose a Siamese Graph Convolution Network (SGCN) for human pose matching...
  • SportsCap

  • Referenced in 1 article [sw40782]
  • introduce a multi-stream spatial-temporal Graph Convolutional Network(ST-GCN) to predict the fine...
  • PiNN

  • Referenced in 2 articles [sw30601]
  • Networks of Molecules and Materials. Atomic neural networks (ANNs) constitute a class of machine learning ... PiNN, we implemented an interpretable graph convolutional neural network variant, PiNet, as well...
  • SyncSpecCnn

  • Referenced in 5 articles [sw26163]
  • with images that are 2D grids, shape graphs are irregular and non-isomorphic data structures ... vertex functions on them by convolutional neural networks, we resort to spectral CNN method that ... kernels in the spectral domain spanned by graph laplacian eigenbases. Under this setting, our network ... different graphs. Towards these goals, we introduce a spectral parameterization of dilated convolutional kernels...
  • RGCNN

  • Referenced in 1 article [sw36662]
  • images before feeding them into neural networks, which leads to voluminous data and quantization artifacts ... instead propose a regularized graph convolutional neural network (RGCNN) that directly consumes point clouds. Leveraging ... point cloud as signals on graph, and define the convolution over graph by Chebyshev polynomial...
  • Chainer Chemistry

  • Referenced in 1 article [sw39646]
  • models (especially GCNN - Graph Convolutional Neural Network) for chemical property prediction...
  • DeepLGP

  • Referenced in 0 articles [sw37448]
  • this study, we present a graph convolutional network (GCN) based method, named DeepLGP, for prioritizing...
  • HACT-Net

  • Referenced in 1 article [sw39634]
  • tissue distribution. Further, a hierarchical graph neural network (HACT-Net) is proposed to efficiently ... proposed method outperformed recent convolutional neural network and graph neural network approaches for breast cancer...
  • DeepSphere

  • Referenced in 2 articles [sw29899]
  • this repository implements a generalization of Convolutional Neural Networks (CNNs) to the sphere. We here ... model the discretised sphere as a graph of connected pixels. The resulting convolution is more ... data at multiple scales. The graph neural network model is based on ChebNet...
  • FeaStNet

  • Referenced in 1 article [sw40969]
  • FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis. Convolutional neural networks (CNNs) have massively ... meshes or other graph-structured data, to which traditional local convolution operators do not directly ... novel graph-convolution operator to establish correspondences between filter weights and graph neighborhoods with arbitrary ... dynamically computed from features learned by the network, rather than relying on predefined static coordinates...
  • CNTK

  • Referenced in 9 articles [sw21056]
  • this directed graph, leaf nodes represent input values or network parameters, while other nodes represent ... model types such as feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs...