Learning Convolutional Neural Networks for Graphs. Revisiting semi-supervised learning with graph embeddings. of semi-supervised classiﬁcation in noisy-graph regimes. GPNNs …  Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 1.Graph Convolutional Networks Zoo. Geometric deep learning: going beyond Euclidean data. Title: Graph Partition Neural Networks for Semi-Supervised Classification.
While learning (unsupervised) graph … Semi-Supervised Classification with Graph Convolutional Networks. Graph convolutional networks papers.  Zhilin Yang, William W Cohen, and Ruslan Salakhutdinov. Data Augmentation based regularization has been shown to be very effective in other types of neural networks but how to apply these techniques in graph neural networks is still under-explored.
Authors: Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander L. Gaunt, Raquel Urtasun, Richard Zemel (Submitted on 16 Mar 2018) Abstract: We present graph partition neural networks (GPNN), an extension of graph neural networks (GNNs) able to handle extremely large graphs.
arXiv preprint arXiv:1609.02907, 2016. 1 Introduction In this work we address the problem of semi-supervised classiﬁcation based on graph convolutional networks (GCNs), as proposed by . Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction Weijia Zhang1, Hao Liu2y, Yanchi Liu3, Jingbo Zhou2, Hui Xiong2y 1University of Science and Technology of China, Hefei, China, 2Business Intelligence Lab, Baidu Research, National Engineering Laboratory of Deep Learning Technology and Application, Beijing, China, 3Rutgers University, USA These architectures alternate between a propagation layer that aggregates the hidden states of the local neighborhood and a fully-connected layer. Our proposed method GraphMix 2 3is inspired by interpolation based data augmentation … These networks can be seen as a ﬁrst-order approximation of the spectral graph convolutional networks developed by , which itself built upon the pioneering work of [2, 9]. Understanding Graph Neural Networks Limitation I: Multiplication with A means that, for every node, we sum up all the feature vectors of all neighboring nodes but not the node itself Fix: Enforce self-loop in the graph by adding identity matrix to A 12. arXiv preprint arXiv:1603.08861, 2016. agnostic regularization techniques for graph neural networks based semi-supervised object classiﬁca-tion. Recently popularized graph neural networks achieve the state-of-the-art accuracy on a number of standard benchmark datasets for graph-based semi-supervised learning, improving significantly over existing approaches. Semi-supervised classiﬁcation with graph convolutional networks. Deep Convolutional Networks on Graph-Structured Data Supervised Graph Attention Network for Semi-Supervised Node Classiﬁcation Dongkwan Kim School of Computing KAIST email@example.com Alice Oh School of Computing KAIST firstname.lastname@example.org Abstract We propose supervised graph attention network (super-GAT), a novel neural net-work architecture for semi-supervised node classiﬁcation in graphs. GitHub URL: * Submit Remove a code repository from this paper ... We present GraphMix, a regularization technique for Graph Neural Network based semi-supervised object classification, leveraging the recent advances in the regularization of classical deep neural networks.