「论文阅读」Dual Contrastive Prediction for Incomplete Multi-View Representation Learning
论文名称：Dual Contrastive Prediction for Incomplete Multi-View Representation Learning 作者：Yijie Lin; Yuanbiao Gou; Xiaotian Liu; Jinfeng Bai; Jiancheng Lv; Xi Peng 期刊：IEEE Transactions on Pattern Analysis and Machine Intelligence 时间：2022-8 原文摘要 In this article, we propose a unifified framework to solve the following two challenging problems in incomplete multi-view representation learning: i) how to learn a consistent representation unifying different views, and ii) how to recover the...
「论文阅读」View-Consistency Learning for Incomplete Multiview Clustering
论文：View-Consistency Learning for Incomplete Multiview Clustering 作者：Ziyu Lv; Quanxue Gao; Xiangdong Zhang; Qin Li; Ming Yang 期刊：IEEE Transactions on Image Processing 时间：2022-7 原文摘要 In this article, we present a novel general framework for incomplete multi-view clustering by integrating graph learning and spectral clustering. In our model, a tensor low-rank constraint are introduced to learn a stable low-dimensional representation, which encodes the complementary information and takes...
「论文阅读」Reinforcement learning on graphs: A survey
论文名称：Reinforcement learning on graphs: A survey 作者：Nie Mingshuo, Chen Dongming, Wang...
「论文阅读」Graph Policy Network for Transferable Active Learning on Graphs
论文名称： Graph Policy Network for Transferable Active Learning on Graphs 作者：Shengding Hu, Zheng Xiong, Meng Qu, Xingdi Yuan, Marc-Alexandre Côté, Zhiyuan Liu, Jian Tang 期刊或会议：NeurIPS 2020 时间：2020-7 代码：ShengdingHu/GraphPolicyNetworkActiveLearning 原文摘要 Graph neural networks (GNNs) have been attracting increasing popularity due to their simplicity and effectiveness in a variety of fifields. However, a large number of labeled data is generally required to train these networks, which could be...
「论文阅读(简)」Policy-GNN: Aggregation Optimization for Graph Neural Networks
论文名称：Policy-GNN: Aggregation Optimization for Graph Neural Networks 作者：Kwei-Herng Lai, Daochen Zha, Kaixiong Zhou, Xia Hu 时间：2020 期刊或会议：KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 代码：https://github.com/datamllab/Policy-GNN 原文摘要： Graph data are pervasive in many real-world applications. Recently, increasing attention has been paid on graph neural networks (GNNs), which aim to model the local graph structures and capture the hierarchical patterns by...
「论文阅读(简)」Learning Robust Representations with Graph Denoising Policy Network
任务与存在问题 任务：图表征学习，目的是学习低维表示，以捕获在原始图上节点直接的依赖性。 存在问题：现有的表征学习方法是基于图神经网络，他们的变体依赖于邻居信息的聚合，这使得他们对图中的噪声非常敏感。 主要工作和创新点 提出了一个新颖的模型GDPNet，用于实现结合了强化学习的鲁棒性图表征学习。GDPNet由两部分组成，分别称为signal neighbor selection和representation learning。GDPNet有效地从噪声图数据中学习了节点的表征 将signal neighbor selection作为一个强化学习问题，使模型能够对待特定任务的奖励信号的弱监督下执行图去噪。 GDPNet能够生成不可见节点的表征 in an inductive fashion，其利用了图结构和节点特特征信息的联系。 证明了GDPNet在数学上等价于求解子模最大化问题，从而保证了模型可以得到有界的最优解。 提出的方法 Graph Denoising Policy Network(GDPNet) 根据signal...
图神经网络
视频学习笔记：https://www.bilibili.com/video/BV1334y1571g?spm_id_from=333.337.search-card.all.click 异构图 包含不同类型节点和连接 异构图的定义：节点类别的数量加上边的类别的数量大于2 元路径(Meta-path)：是连接两个对象的复合关系，是一种广泛使用的捕获语义的结构，比如，电影——演员——电影，这样的一个连接 基于元路径的邻居(Meta-path based Neighbors)：通过Meta-path找到的邻居信息 Heterogeneous Graph Attention Network（HAN） 这个算法主要包含了两个重要的计算方式，一个是节点级别的注意力(Node-level Attention)，另一个是语义级别的注意力(Semantic-level...
「论文阅读(简)」Vision GNN: An Image is Worth Graph of Nodes
期刊：Vision GNN: An Image is Worth Graph of Nodes 作者：Kai Han, Yunhe Wang, Jianyuan Guo, Yehui Tang, Enhua Wu 时间：2022. 7 期刊：arxiv 代码：https://github.com/huawei-noah/Efficient-AI-Backbones 原文摘要 Network architecture plays a key role in the deep learning-based computer vision system. The widely-used convolutional neural network and transformer treat the image as a grid or sequence structure, which is not flexible to capture irregular and complex objects. In this paper, we propose to represent...
「论文阅读(简)」Graph Attention Network
论文名称：Graph Attention Networks 作者：Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio 时间：2018 期刊或会议：ICLR 代码：https://github.com/Diego999/pyGAT 原文摘要 We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able...
「论文阅读（简）」Semi-Supervised Classification with Graph Convolutional Network
论文名称：Semi-Supervised Classification with Graph Convolutional Network 作者：Kipf, Thomas N., Max Welling 时间：2017年 期刊或会议：ICLR 代码：https://github.com/dragen1860/GCN-PyTorch 原文摘要 We present a scalable approach for semi-supervised learning on graph-structured data that is based on an effificient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized fifirst-order approximation of spectral graph...