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Top Papers in Graph convolutional networks

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Lorentzian Graphal Network for Hyperbolic Geometry

Lorentzian Graph Convolutional Networks

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Deformable Graph Convolutional Networks

Graph neural networks (GNNs) have significantly improved the representation
power for graph-structured data. Despite of the recent success of GNNs, the
graph convolution in most GNNs have two limitati

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Distributed Graph Convolutional Networks

The aim of this work is to develop a fully-distributed algorithmic framework
for training graph convolutional networks (GCNs). The proposed method is able
to exploit the meaningful relational structur

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Simplifying Graph Convolutional Networks

Graph Convolutional Networks (GCNs) and their variants have experienced
significant attention and have become the de facto methods for learning graph
representations. GCNs derive inspiration primarily

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Spiking Graph Convolutional Networks

Graph Convolutional Networks (GCNs) achieve an impressive performance due to
the remarkable representation ability in learning the graph information.
However, GCNs, when implemented on a deep network,

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Semi-Riemannian Geometry for Graph Conjunctive Networks

Semi-Riemannian Graph Convolutional Networks

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Sheaf Neural Networks

We present a generalization of graph convolutional networks by generalizing
the diffusion operation underlying this class of graph neural networks. These
sheaf neural networks are based on the sheaf L

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Topology Adaptive Graph Convolutional Networks

Spectral graph convolutional neural networks (CNNs) require approximation to
the convolution to alleviate the computational complexity, resulting in
performance loss. This paper proposes the topology

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Graph Convolutional Networks for Text Classification

Text classification is an important and classical problem in natural language
processing. There have been a number of studies that applied convolutional
neural networks (convolution on regular grid, e

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Differentiable Graph Module (DGM) Graph Convolutional Networks

Graph deep learning has recently emerged as a powerful ML concept allowing to generalize successful deep neural architectures to non-Euclidean structured data. Such methods have shown promising result

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Connecting Graph Convolutional Networks and Graph-Regularized PCA

Graph convolution operator of the GCN model is originally motivated from a
localized first-order approximation of spectral graph convolutions. This work
stands on a different view; establishing a \tex

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Graph Convolutional Networks for Hyperspectral Image Classification

Convolutional neural networks (CNNs) have been attracting increasing attention in hyperspectral (HS) image classification, owing to their ability to capture spatial-spectral feature representations. N

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Modeling Relational Data with Graph Convolutional Networks

Knowledge graphs enable a wide variety of applications, including question
answering and information retrieval. Despite the great effort invested in their
creation and maintenance, even the largest (e

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Simple and Deep Graph Convolutional Networks

Graph convolutional networks (GCNs) are a powerful deep learning approach for
graph-structured data. Recently, GCNs and subsequent variants have shown
superior performance in various application areas

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SoGCN: Second-Order Graph Convolutional Networks

Graph Convolutional Networks (GCN) with multi-hop aggregation is more
expressive than one-hop GCN but suffers from higher model complexity. Finding
the shortest aggregation range that achieves compara

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Reward Propagation Using Graph Convolutional Networks

Potential-based reward shaping provides an approach for designing good reward
functions, with the purpose of speeding up learning. However, automatically
finding potential functions for complex enviro

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Mutual Teaching for Graph Convolutional Networks

Graph convolutional networks produce good predictions of unlabeled samples
due to its transductive label propagation. Since samples have different
predicted confidences, we take high-confidence predic

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GraphMix: Improved Training of GNNs for Semi-Supervised Learning

We present GraphMix, a regularization method for Graph Neural Network based
semi-supervised object classification, whereby we propose to train a
fully-connected network jointly with the graph neural n

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BayesGrad: Explaining Predictions of Graph Convolutional Networks

Recent advances in graph convolutional networks have significantly improved
the performance of chemical predictions, raising a new research question: "how
do we explain the predictions of graph convol

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JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks

Graph Convolutional Network (GCN) has exhibited strong empirical performance
in many real-world applications. The vast majority of existing works on GCN
primarily focus on the accuracy while ignoring

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