This work presents Hierarchical Graph Neural Networks (HGNN) 鈥 a novel methodology for location-aware collaborative user-aspect data fusion and location prediction. It incorporates geographical location information of users and clustering effect of regions and can capture topological relations while ...
基于GNN的层次人脸聚类-Learning Hierarchical Graph Neural Networks for Image Clustering 柠濛 来自专栏 · 机器学习知识共同富裕 25 人赞同了该文章 一、简介 本次介绍的文章来自CVPR 2021,是目前图像聚类领域比较新的一篇文章,作者来自亚马逊aws。 本文提出了一种有监督的层次GNN模型,使用一种新方法融合每一层的...
《Learning Effective Road Network Representation with Hierarchical Graph Neural Networks》 发表于 KDD 2020 会议上。 该文章提出了一种新的路网表征模型。该模型首次将多层图神经网络应用于路网表征中。其分别从 raod segments, structural regions, functional zones 三个层次对路网进行建模表征。
【论文阅读】Learning Effective Road Network Representation with Hierarchical Graph Neural Networks,Hello!ଘ(੭ˊᵕˋ)੭昵称:海轰标签:程序猿|C++选手|学生简介:因C语言结识编程,随后转入计算机专业,获得过国家奖学金
Recently, there has been a promising tendency to generalize convolutional neural networks (CNNs) to graph domain. However, most of the methods cannot obtain adequate global information due to their shallow structures. In this paper, we address this challenge by proposing a hierarchical graph ...
Learning Hierarchical Graph Neural Networks for Image ClusteringYifan Xing * Tong He * Tianjun Xiao Yongxin Wang Yuanjun XiongWei Xia David Wipf Zheng Zhang Stefano SoattoAmazon Web Services{yifax, htong, tianjux, yongxinw, yuanjx, wxia, daviwipf, zhaz, soattos}@amazon.comAbstractWe propose...
In recent years, graph convolutional networks (GCNs) have made remarkable achievements in the hyperspectral image (HSI) classification task. However, existing GCN-based methods cannot adequately encode similarity edge relationship between superpixels, and few of them use hierarchical mechanism to extract ...
Here we describe a generic DL platform tailored for predicting PPIs, Hierarchical Graph Neural Networks for Protein–Protein Interactions (HIGH-PPI). HIGH-PPI models the structural protein representations with the bottom inside-of-protein view GNNs (BGNN) and the PPI network with the top outside-...
And we design a new architecture, Hierarchical Graph Neural Network (HGNN), which can predict the stock type by integrating multiple levels of market state at hierarchical view. One view called node view is to strengthen the importance of target stock itself based on historical sequence. The ...
Requiring only a single forward pass per time step, Graph-EFM allows for fast generation of arbitrarily large ensembles. We experiment with the model on both global and limited area forecasting. Ensemble forecasts from Graph-EFM achieve equivalent or lower errors than comparable deterministic models,...