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Introduction

Semi-supervised Learning with Graph Learning-Convolutional Networks Bo Jiang, Ziyan Zhang, Doudou Lin, Jin Tang∗and Bin Luo School of Computer Science and Technology, Anhui University, Hefei, 230601, China jiangbo@ahu.edu.cn,{zhangziyanahu,ahulindd}@163.com,ahhftang@gmail.com,luobin@ahu.edu.cn …

Abstract. Graph Convolutional Neural Networks (graph CNNs) have been widely used for graph data representation and semi-supervised learning tasks.

TensorFlow implementation of several popular Graph Neural Network layers, wrapped with tf.keras.layers.Layer. 0. Report inappropriate ...

In this paper, we propose a novel Graph Learning- Convolutional Network (GLCN) for semi-supervised learn- ing problem. The main idea of GLCN is to learn an optimal graph representation that best serves graph CNNs for semi- supervised learning by integrating bothgraph learningand graph convolutionsimultaneously in a unified network ar- chitecture.

Deeper insights into graph convolutional networks for semi-supervised learning. In AAAI'18. • Kenta Oono and Taiji Suzuki. Graph neural networks ...

Contribute to jiangboahu/GLCN-tf development by creating an account on GitHub. ... Graph Learning-Convolutional Networks for the task of (semi-supervised) ...

We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks ...

Library for deep learning on graphs. ... Attention-based Graph Neural Network for Semi-supervised Learning, node classification.

Graph Convolutional Neural Networks (graph CNNs) have been widely used for graph data representation and semi-supervised learning tasks.

10.01.2022 · Star 971. Code. Issues. Pull requests. An up-to-date & curated list of awesome semi-supervised learning papers, methods & resources. machine-learning natural-language-processing computer-vision deep-learning generative-model semi-supervised-learning graph-neural-networks. Updated on Nov 20, 2021.

A graph-based semi-supervised learning for question-answering, in ACL/IJNLP, 2009. Celikyilmaz, A., Thint, M. and Huang, Z. paper. A graph-based semi-supervised learning for question semantic labeling, in NAACL, 2010. Celikyilmaz, A. and Hakkani-Tur, D. paper. Graph-based semi-supervised learning of translation models from monolingual data, in ...

May 22, 2019 · Confidence-based Graph Convolutional Networks for Semi-Supervised Learning. Source code for AISTATS 2019 paper: Confidence-based Graph Convolutional Networks for Semi-Supervised Learning. Label prediction on node a by Kipf-GCN and ConfGCN (this paper). L0 is a’s true label.

graph-neural-networks,Drench yourself in Deep Learning, Reinforcement Learning, ... graph-neural-networks,Convolutional Neural Networks on Graphs with Fast ...

Star 971. Code. Issues. Pull requests. An up-to-date & curated list of awesome semi-supervised learning papers, methods & resources. machine-learning natural-language-processing computer-vision deep-learning generative-model semi-supervised-learning graph-neural-networks. Updated on Nov 20, 2021.

Graph Convolutional Neural Networks (graph CNNs) have been widely used for graph data representation and semi-supervised learning tasks. However, existing graph CNNs generally use a fixed graph which may not be optimal for semi-supervised learning tasks. ..

Graph Convolutional Neural Networks (graph CNNs) have been widely used for graph data representation and semi-supervised learning tasks. However, existing graph CNNs generally use a fixed graph which may not be optimal for semi-supervised learning tasks. .. read more PDF Abstract Code No code implementations yet. Submit your code now Tasks

25.05.2020 · Introduce convolution directly on graphs. See also. Learning Curve Theory; Neural Topic Modeling with Continual Lifelong Learning; Wide Open Spaces: A Statistical Technique for Measuring Space Creation in Professional Soccer