GKT 本文。 GKT的体系结构如下： 设置 要运行此代码，您需要以下内容： 配备GPU的机器 python3 numpy，pandas，scipy，scikit-learn和火炬程序包： pip3 install numpy==1.17.4 pandas==1.1.2 scipy==1.5.2 scikit-learn==0.23.2 torch==1.4.0 请注意，不要使用0.23.4版本的熊猫，因为在processing.py文件中执行以下命令时，它将导致错误。 df.groupby('user_id', axis=0).apply(get_data) 如果您使用“ assistment_test15.csv”文件进行测试，则在pandas 0.23.4版本中，经过groupby用户后，它将返回16名学生。 但是，如果您在1.x版本中使用熊猫，它将返回15名学生。 （此

Focused on the mathematical foundations of social media analysis, Graph-Based Social Media Analysis provides a comprehensive introduction to the use of graph analysis in the study of social and digital media. It addresses an important scientific and technological challenge, namely the confluence of graph analysis and network theory with linear algebra, digital media, machine learning, big data analysis, and signal processing. Supplying an overview of graph-based social media analysis, the book provides readers with a clear understanding of social media structure. It uses graph theory, particularly the algebraic description and analysis of graphs, in social media studies. The book emphasizes the big data aspects of social and digital media. It presents various approaches to storing vast amounts of data online and retrieving that data in real-time. It demystifies complex social media phenomena, such as information diffusion, marketing and recommendation systems in social media, and evolving systems. It also covers emerging trends, such as big data analysis and social media evolution. Describing how to conduct proper analysis of the social and digital media markets, the book provides insights into processing, storing, and visualizing big social media data and social graphs. It includes coverage of graphs in social and digital media, graph and hyper-graph fundamentals, mathematical foundations coming from linear algebra, algebraic graph analysis, graph clustering, community detection, graph matching, web search based on ranking, label propagation and diffusion in social media, graph-based pattern recognition and machine learning, graph-based pattern classification and dimensionality reduction, and much more. This book is an ideal reference for scientists and engineers working in social media and digital media production and distribution. It is also suitable for use as a textbook in undergraduate or graduate courses on digital media, social media, or social networks. Table of Contents Chapter 1 - Graphs in Social and Digital Media Chapter 2 - Mathematical Preliminaries: Graphs and Matrices Chapter 3 - Algebraic Graph Analysis Chapter 4 - Web Search Based on Ranking Chapter 5 - Label Propagation and Information Diffusion in Graphs Chapter 6 - Graph-Based Pattern Classification and Dimensionality Reduction Chapter 7 - Matrix and Tensor Factorization with Recommender System Applications Chapter 8 - Multimedia Social Search Based on Hypergraph Learning Chapter 9 - Graph Signal Processing in Social Media Chapter 10 - Big Data Analytics for Social Networks Chapter 11 - Semantic Model Adaptation for Evolving Big Social Data Chapter 12 - Big Graph Storage, Processing and Visualization

2013年经典CVPR文章代码 Saliency Detection via Graph-Based Manifold Ranking

暗通道matlab代码基于图的盲图像去模糊 该代码是我们的TIP论文“从单张照片中基于图的盲图像去模糊”的升级实现。 先决条件 Matlab（> = R2015a） 运行测试 Step 1. run graph_blind_main.m Step 2. select a blurred image 参数 用户只需要调整一个参数。 在第21行，估计的内核大小k_estimate_size 。 该k_estimate_size必须比真正的内核大小（默认值为69）放大。 为了获得最佳性能，请将该值设置为接近实际内核大小，并稍大一些。 如果要关闭中间输出，可以在第22行设置show_intermediate = false 。 关于噪音 为了使噪声更强健，我们在本文之外增加了一些降噪模块。 我们嵌入了一个去噪电视，以对输入图像进行预处理。 我们为中间输出内核添加了一个小波域过滤。 我们添加了一个蒙版来过滤梯度域中的小/噪声梯度。 诸如BM3D之类的更复杂的去噪功能可以由用户预先完成。 关于非盲图像去模糊 在使用提出的算法进行内核估计之后，我们使用最新技术来进行非盲图像去模糊。 在这里，我们为用

对高光谱图像执行基于 SLIC 超像素的降维，然后是基于 SVM 的分类，如论文中所述： X. Zhang、SE Chew、Z. Xu 和 ND Cahill，“SLIC Superpixels for Efficient Graph-Based Dimensionality Reduction of Hyperspectral Imagery”，Proc。 SPIE 防御与安全：多光谱、高光谱和超光谱图像的算法和技术 XXI，2015 年 4 月。

While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer vision, natural language processing, and other areas of Artificial Intelligence. Recognizing this promising and emerging area of research, this synthesis lecture focuses on graphbased SSL algorithms (e.g., label propagation methods). Our hope is that after reading this book, the reader will walk away with the following: (1) an in-depth knowledge of the current stateof- the-art in graph-based SSL algorithms, and the ability to implement them; (2) the ability to decide on the suitability of graph-based SSL methods for a problem; and (3) familiarity with different applications where graph-based SSL methods have been successfully applied.

This book provides a definition and study of a knowledge representation and reasoning formalism stemming from conceptual graphs, while focusing on the computational properties of this formalism.

Saliency Detection via Graph-Based Manifold Ranking对应的源码

GrabCut源代码 vs2008，vs2010 都可打开 GrabCut是目前图论中较前沿的分割算法 GrabCut源代码 vs2008，vs2010 都可打开 GrabCut是目前图论中较前沿的分割算法

GMC: Graph-based Multi-view Clustering 源代码