Linkedin Learning - Data Analytics Graph Analytics-XQZT
English | Size: 247.05 MB
Category: Tutorial

English | 2021 | ISBN: 1617295647 | 861 pages | True EPUB , MOBI | 47.33 MB
Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data.

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 27 lectures (1h 8m) | Size: 603.2 MB
A concise introduction to knowledge graphs for complete beginners

English | 2021 | ISBN: 1138361402 | 421 pages | True EPUB | 15.16 MB
Graph theory is the study of interactions, conflicts, and connections. The relationship between collections of discrete objects can inform us about the overall network in which they reside, and graph theory can provide an avenue for analysis. This text, for the first undergraduate course, will explore major topics in graph theory from both a theoretical and applied viewpoint. Topics will progress from understanding basic terminology, to addressing computational questions, and finally ending with broad theoretical results. Examples and exercises will guide the reader through this progression, with particular care in strengthening proof techniques and written mathematical explanations. Current applications and exploratory exercises are provided to further the reader's mathematical reasoning and understanding of the relevance of graph theory to the modern world. Features 　, The first chapter introduces graph terminology, mathematical modeling using graphs, and a review of proof techniques featured throughout the book, The second chapter investigates three major route problems: eulerian circuits, hamiltonian cycles, and shortest paths. The third chapter focuses entirely on trees - terminology, applications, and theory. Four additional chapters focus around a major graph concept: connectivity, matching, coloring, and planarity. Each chapter brings in a modern application or approach. , Hints and Solutions to selected exercises provided at the back of the book.
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MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 240 MB | Duration: 18 Lessons | 59m
In this course, you'll learn how to use ggDescription in Python to create data visualizations using a grammar of graphics. A grammar of graphics is a high-level tool that allows you to create data Descriptions in an efficient and consistent way. It abstracts most low-level details, letting you focus on creating meaningful and beautiful visualizations for your data.

Research Trends in Graph Theory and Applications
English | 2021 | ISBN: 3030779823 | 412 Pages | EPUB | 7.33 MB
The Workshop for Women in Graph Theory and Applications was held at the Institute for Mathematics and Its Applications (University of Minnesota, Minneapolis) on August 19-23, 2019. During this five-day workshop, 42 participants performed collaborative research, in six teams, each focused on open problems in different areas of graph theory and its applications. The research work of each team was led by two experts in the corresponding area, who prior to the workshop, carefully selected relevant and meaningful open problems that would yield high-quality research and results of strong impact. As a result, all six teams have made significant contributions to several open problems in their respective areas. The workshop led to the creation of the Women in Graph Theory and Applications Research Collaboration Network, which provided the framework to continue collaborating and to produce this volume.

Algebraic Graph Theory Chris Godsil Gordon Royle 2001
pdf | 39.21 MB | English | Isbn:‎ 978-0387952208 | Author: Chris Godsil | Year: 2001

English | 2021 | ISBN: 1789450284 | 325 pages | True PDF | 11.49 MB
Graph spectral image processing is the study of imaging data from a graph frequency perspective. Modern image sensors capture a wide range of visual data including high spatial resolution/high bit-depth 2D images and videos, hyperspectral images, light field images and 3D point clouds. The field of graph signal processing - extending traditional Fourier analysis tools such as transforms and wavelets to handle data on irregular graph kernels - provides new flexible computational tools to analyze and process these varied types of imaging data. Recent methods combine graph signal processing ideas with deep neural network architectures for enhanced performances, with robustness and smaller memory requirements.

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 26 lectures (4h 29m) | Size: 1.73 GB
From Graph Representation Learning to Graph Neural Network (Complete Introductory Course to GNN)

Graph-Powered Machine Learning | 26.28 MB
English | 493 Pages

Title: Graph-Powered Machine Learning
Author: Alessandro Negro
Year: 2020