The dataset uses NLP and Text Mining technology to develop Chinese Medical Knowledge Graph. user guide.pdf

FullBNT-1.0.7下载及draw_graph箭头显示问题-附件资源

知识图谱一直是学术界和工业界关注的热点。随着AAAI2020的到来，以下整理了最新10篇关于知识图谱的论文，来自清华大学、中科大、北航、中山大学、UCL、Facebook、腾讯、阿里巴巴等，包含义原知识图谱、知识图谱表示学习、知识迁移、知识图谱层次表示、常识知识图谱补全等，请大家查看！

当然只是我刷了点小聪明，编辑了一下原版的大师编的pdf,但不好打印啊，所以就想办法折腾了一下啊，终于能A4单面双页打印省钱了啊，呵呵，这本书很经典啊，给大家分享啊

Accenture-2021-Canadian-Fintech-Report-Charts-Graph.pdf

Algorithms Illuminated (Part 2): Graph Algorithms and Data Structures (Volume 2) By 作者: Tim Roughgarden ISBN-10 书号: 0999282921 ISBN-13 书号: 9780999282922 Edition 版本: First 出版日期: 2018-08-05 pages 页数: (222 ) $17.99 Algorithms are the heart and soul of computer science. Their applications range from network routing and computational genomics to public-key cryptography and machine learning. Studying algorithms can make you a better programmer, a clearer thinker, and a master of technical interviews. Algorithms Illuminated is an accessible introduction to the subject for anyone with at least a little programming experience. The exposition emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details—like a transcript of what an expert algorithms tutor would say over a series of one-on-one lessons. The book includes solutions to all quizzes and selected problems, and a series of YouTube videos by the author accompanies the book. Part 2 of this book series covers graph search and its applications, shortest-path algorithms, and the applications and implementation of several data structures: heaps, search trees, hash tables, and bloom filters.

该项目包含由应用计算机科学研究所在Łódź技术大学开发的网络图形压缩算法的实现。

The primary aim of this book is to present a coherent introduction to graph theory, suitable as a textbook for advanced undergraduate and beginning graduate students in mathematics and computer science. It provides a systematic treatment of the theory of graphs without sacrificing its intuitive and aesthetic appeal. Commonly used proof techniques are described and illustrated. The book also serves as an introduction to research in graph theory.

Title: R Graph Cookbook, 2nd Edition Author: Hrishi V. Mittal, Jaynal Abedin Length: 368 pages Edition: 2 Language: English Publisher: Packt Publishing Publication Date: 2014-10-20 ISBN-10: 1783988789 ISBN-13: 9781783988785 Over 70 recipes for building and customizing publication-quality visualizations of powerful and stunning R graphs About This Book Create a wide range of powerful R graphs Leverage lattice and ggplot2 to create high-quality graphs. Develop well-structured maps for efficient data visualization Who This Book Is For Targeted at those with an existing familiarity with R programming, this practical guide will appeal directly to programmers interested in learning effective data visualization techniques with R and a wide-range of its associated libraries. In Detail Data visualization is one of the most important tasks in the data science track. Through effective visualization, we can easily uncover underlying patterns among variables without doing any sophisticated statistical analysis. Starting with a high-level overview of the R graphics system and then moving through this practical cookbook, you will leverage powerful default R graphics and utilize advanced graphics systems such as lattice and ggplot2, the grammar of graphics. Through inspecting large datasets using tableplot and stunning three-dimensional visualizations, you will know how to produce, customize, and publish advanced visualizations using this popular, and powerful, framework. Table of Contents Chapter 1. R Graphics Chapter 2. Basic Graph Functions Chapter 3. Beyond the Basics – Adjusting Key Parameters Chapter 4. Creating Scatter Plots Chapter 5. Creating Line Graphs and Time Series Charts Chapter 6. Creating Bar, Dot, and Pie Charts Chapter 7. Creating Histograms Chapter 8. Box and Whisker Plots Chapter 9. Creating Heat Maps and Contour Plots Chapter 10. Creating Maps Chapter 11. Data Visualization Using Lattice Chapter 12. Data Visualization Using ggplot2 Chapter 13. Inspecting

Graph_Matching_Network-Pytorch 图匹配网络的Pytorch实现