Adaptive Graph Convolutional Neural Networks

[1801.03226] Adaptive Graph Convolutional Neural Networks - arXiv

The paper proposes a generalized and flexible graph CNN taking data of arbitrary graph structure as input. In that way a task-driven adaptive ...

Adaptive Graph Convolutional Neural Networks - AAAI

The paper proposes a generalized and flexible graph CNN taking data of arbitrary graph structure as input. In that way a task-driven adaptive ...

AGCN Explained - Papers With Code

Adaptive Graph Convolutional Neural Networks ... AGCN is a novel spectral graph convolution network that feed on original data of diverse graph structures. Image ...

(PDF) Adaptive Graph Convolutional Neural Networks - ResearchGate

Graph convolutional network (GCN) is generalization of convolutional neural network (CNN) to work with arbitrarily structured graphs. A binary ...

codemarsyu/Adaptive-Graph-Convolutional-Network: AGCN - GitHub

AGCN - Spectral ChevNet built on Adaptive, trainable graphs - codemarsyu/Adaptive-Graph-Convolutional-Network.

Adaptive graph convolutional neural networks - ACM Digital Library

Abstract. Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and ...

Adaptive Graph Convolutional Recurrent Network for Traffic ...

Recent works focus on designing com- plicated graph neural network architectures to capture shared patterns with the help of pre-defined graphs. In this paper, ...

Topology Adaptive Graph Convolutional Networks - arXiv

Abstract:Spectral graph convolutional neural networks (CNNs) require approximation to the convolution to alleviate the computational ...

Adaptive Graph Convolutional Network With Attention Graph ...

Convolutional graph neural networks (GCN- s) [4, 8, 29, 31, 1, 45, 16] are a variant of GNNs, and aim to generalize convolution to graph domain. Algorithms ...

Adaptive filters in Graph Convolutional Neural Networks

This paper presents a novel method to adapt the behaviour of a ConvGNN to the input performing spatial convolution on graphs using input-specific filters.

Drug repositioning with adaptive graph convolutional networks

With the rapid development of deep learning, graph convolutional networks (GCNs) have been widely adopted for drug repositioning tasks. However, prior GCNs ...

Unsupervised Domain Adaptive Graph Convolutional Networks

Experimental results on real-world datasets in the node classification task validate the performance of our method, compared to state-of-the-art graph neural ...

Adaptive Graph Convolutional Neural Networks - Semantic Scholar

A generalized and flexible graph CNN taking data of arbitrary graph structure as input is proposed, in that way a task-driven adaptive graph is learned for ...

Two-Stream Adaptive Graph Convolutional Networks for Skeleton ...

Graph convolutional neural networks. There have been many works on graph convolution, and the principle of constructing GCNs mainly follows two streams ...

EAGCN: An Efficient Adaptive Graph Convolutional Network for Item ...

Recently, emerged graph neural networks (GNNs) shine a light on simulating the recursive social diffusion process, to refine user embedding ...

adaptive graph convolutional neural network and its biomedical ...

As the rise of graph neural networks, many deep learning frameworks have been extended to graph-structured data. The research in many diverse regimes have ...

Adaptive Graph Convolutional Recurrent Network for Traffic ...

Recent works focus on designing complicated graph neural network architectures to capture shared patterns with the help of pre-defined graphs. In this paper, we ...

Spatial adaptive graph convolutional network for skeleton-based ...

In this paper, a spatially adaptive residual graph convolutional network (SARGCN) is proposed for action recognition based on skeleton feature ...

Topology Adaptive Graph Convolutional Networks - OpenReview

This paper proposes the topology adaptive graph convolutional network (TAGCN), a novel graph convolutional network that generalizes CNN architectures to graph- ...

AAGCN: a graph convolutional neural network with adaptive feature ...

It is a graph convolutional neural network that combines sampling and feature aggregation, improving computational efficiency by sampling and ...