-
# On the Global Self-attention Mechanism for Graph Convolutional Networks [[Wang+, 20](https://arxiv.org/abs/2010.10711)]
## Abstract
- Apply Global self-attention (GSA) to GCNs
- GSA allows GCNs…
-
Please add explicit support for graph-structured data including
- quantum graph kernels
- quantum graph neural networks, particularly quantum graph convolutional networks
-
## 一言でいうと
Graph Convolutionをプロダクションレベル(30億ノード！)で使用したという金字塔的な論文(Pinterestで使われている)。グラフサイズが膨大なので近傍ノードをサンプリング(ランダムウォーク)で収集しており、収集はCPU・畳み込みはGPUで役割分担しMapReduceで分散処理して高速化している。
### 論文リンク
https://arxi…
-
What do you think of adding Graph Convolutional Networks in a new section on ML and deep learning?
Here are some references:
* https://tkipf.github.io/graph-convolutional-networks/
* https://gi…
-
* [ ] 1. [Hierarchical Pooling in Graph Neural Networks to Enhance Classification Performance in Large Datasets](https://www.mdpi.com/1424-8220/21/18/6070/htm)
* [ ] 2. [Hierarchical Graph Pooling wi…
-
I found that graph convolution functions are available in Keras using Python. Is it possible to use it in R? Thanks!
-
Title: - Semi-supervised User Geolocation via Graph Convolutional Networks
Year: - 2018
Venue: - arXiv
**Main Problem**
The authors addresses the problem of inaccurate prediction of user geo-loc…
-
## 🚀 Feature
Multi-dimensional Graph Convolutional Networks
Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty
## Motivation
In many of the re…
-
I think it's a reasonable claim that all graph convolutional networks (GCN) are graph neural networks (GNN), since they operate on graphs, and are NNs. However, there are graph neural networks that do…
-
'Convolution in the Cloud: Learning Deformable Kernels in 3D Graph Convolution Networks for Point Cloud Analysis' is a good paper with 3D-GCN, whose funciton contain graph classification and node ca…