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Results 1-8 (8 total)
lgli/Graph Algorithms the Fun Way Powerful Algorithms Decoded, Not Oversimplified (Jeremy Kubica).azw
Graph Algorithms the Fun Way: Powerful Algorithms Decoded, Not Oversimplified Kubica, Jeremy No Starch Press, Incorporated, 2024
Enter the wonderful world of graph algorithms, where you’ll learn when and how to apply these highly useful data structures to solve a wide range of fascinating (and fantastical) computational problems. *** Graph Algorithms the Fun Way offers a refreshing approach to complex concepts by blending humor, imaginative examples, and practical Python implementations to reveal the power and versatility of graph based problem-solving in the real world. Through clear diagrams, engaging examples, and Python code, you’ll build a solid foundation for addressing graph problems in your own projects. *** Explore a rich landscape of cleverly constructed scenarios where: *** Hedge mazes illuminate depth-first search Urban explorations demonstrate breadth-first search Intricate labyrinths reveal bridges and articulation points Strategic planning illustrates bipartite matching *** From fundamental graph structures to advanced topics, you will: Implement powerful algorithms, including Dijkstra’s, A*, and Floyd-Warshall Tackle puzzles and optimize pathfinding with newfound confidence Uncover real-world applications in social networks and transportation systems Develop robust intuition for when and why to apply specific graph techniques *** Delve into topological sorting, minimum spanning trees, strongly connected components, and random walks. Confront challenges like graph coloring and the traveling salesperson problem. *** Prepare to view the world through the lens of graphs—where connections reveal insights and algorithms unlock new possibilities.
Read more…
English [en] · AZW · 16.8MB · 2024 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/zlib · Save
base score: 11050.0, final score: 167691.69
Graph Algorithms the Fun Way: Powerful Algorithms Decoded, Not Oversimplified Jeremy Kubica No Starch Press, 2024
English [en] · EPUB · 4.6MB · 2024 · 📘 Book (non-fiction) · 🚀/zlib · Save
base score: 11062.0, final score: 167630.6
Graph Algorithms the Fun Way: Powerful Algorithms Decoded, Not Oversimplified Jeremy Kubica No Starch Press
English [en] · PDF · 48.0MB · 📗 Book (unknown) · 🚀/zlib · Save
base score: 11060.0, final score: 167623.75
lgli/GraphAlgorithmstheFunWay.mobi
Graph Algorithms the Fun Way: Powerful Algorithms Decoded, Not Oversimplified Jeremy Kubica No Starch Press, Incorporated, 1, 2024
Enter the wonderful world of graph algorithms, where you’ll learn when and how to apply these highly useful data structures to solve a wide range of fascinating (and fantastical) computational problems. Graph Algorithms the Fun Way offers a refreshing approach to complex concepts by blending humor, imaginative examples, and practical Python implementations to reveal the power and versatility of graph based problem-solving in the real world. Through clear diagrams, engaging examples, and Python code, you’ll build a solid foundation for addressing graph problems in your own projects. Explore a rich landscape of cleverly constructed scenarios where: Hedge mazes illuminate depth-first search Urban explorations demonstrate breadth-first search Intricate labyrinths reveal bridges and articulation points Strategic planning illustrates bipartite matching From fundamental graph structures to advanced topics, you will: Implement powerful algorithms, including Dijkstra’s, A*, and Floyd-Warshall Tackle puzzles and optimize pathfinding with newfound confidence Uncover real-world applications in social networks and transportation systems Develop robust intuition for when and why to apply specific graph techniques Delve into topological sorting, minimum spanning trees, strongly connected components, and random walks. Confront challenges like graph coloring and the traveling salesperson problem. Prepare to view the world through the lens of graphs—where connections reveal insights and algorithms unlock new possibilities.
Read more…
English [en] · MOBI · 77.9MB · 2024 · 📘 Book (non-fiction) · 🚀/lgli/lgrs · Save
base score: 11055.0, final score: 167614.78
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lgli/Graph Algorithms the Fun Way - Jeremy Kubica;.epub
Graph Algorithms the Fun Way: Powerful Algorithms Decoded, Not Oversimplified Jeremy Kubica Penguin Random House, 2024
Enter the wonderful world of graph algorithms, where you’ll learn when and how to apply these highly useful data structures to solve a wide range of fascinating (and fantastical) computational problems. Graph Algorithms the Fun Way offers a refreshing approach to complex concepts by blending humor, imaginative examples, and practical Python implementations to reveal the power and versatility of graph based problem-solving in the real world. Through clear diagrams, engaging examples, and Python code, you’ll build a solid foundation for addressing graph problems in your own projects. Explore a rich landscape of cleverly constructed scenarios where • Hedge mazes illuminate depth-first search • Urban explorations demonstrate breadth-first search • Intricate labyrinths reveal bridges and articulation points • Strategic planning illustrates bipartite matching From fundamental graph structures to advanced topics, you will • Implement powerful algorithms, including Dijkstra’s, A*, and Floyd-Warshall • Tackle puzzles and optimize pathfinding with newfound confidence • Uncover real-world applications in social networks and transportation systems • Develop robust intuition for when and why to apply specific graph techniques Delve into topological sorting, minimum spanning trees, strongly connected components, and random walks. Confront challenges like graph coloring and the traveling salesperson problem. Prepare to view the world through the lens of graphs—where connections reveal insights and algorithms unlock new possibilities.
Read more…
English [en] · EPUB · 28.2MB · 2024 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/zlib · Save
base score: 11065.0, final score: 167605.77
lgli/Graph_Algorithms_the_Fun_Way.epub
Graph Algorithms the Fun Way: Powerful Algorithms Decoded, Not Oversimplified Jeremy Kubica No Starch Press, Incorporated, 2025
English [en] · EPUB · 45.8MB · 2025 · 📘 Book (non-fiction) · 🚀/lgli/lgrs · Save
base score: 11062.0, final score: 167604.92
lgli/Graph.Algorithms.the.Fun.Way.Sanet.st.epub
Graph Algorithms the Fun Way: Powerful Algorithms Decoded, Not Oversimplified Jeremy Kubica No Starch Press, Incorporated, 2024
Enter the wonderful world of graph algorithms, where you’ll learn when and how to apply these highly useful data structures to solve a wide range of fascinating (and fantastical) computational problems. Graph Algorithms the Fun Way offers a refreshing approach to complex concepts by blending humor, imaginative examples, and practical Python implementations to reveal the power and versatility of graph based problem-solving in the real world. Through clear diagrams, engaging examples, and Python code, you’ll build a solid foundation for addressing graph problems in your own projects. Explore a rich landscape of cleverly constructed scenarios where: Hedge mazes illuminate depth-first search Urban explorations demonstrate breadth-first search Intricate labyrinths reveal bridges and articulation points Strategic planning illustrates bipartite matching From fundamental graph structures to advanced topics, you will: Implement powerful algorithms, including Dijkstra’s, A*, and Floyd-Warshall Tackle puzzles and optimize pathfinding with newfound confidence Uncover real-world applications in social networks and transportation systems Develop robust intuition for when and why to apply specific graph techniques Delve into topological sorting, minimum spanning trees, strongly connected components, and random walks. Confront challenges like graph coloring and the traveling salesperson problem. Prepare to view the world through the lens of graphs—where connections reveal insights and algorithms unlock new possibilities.
Read more…
English [en] · EPUB · 31.3MB · 2024 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/zlib · Save
base score: 11065.0, final score: 167587.61
lgli/GraphAlgorithmstheFunWay.pdf
Graph Algorithms the Fun Way: Powerful Algorithms Decoded, Not Oversimplified Jeremy Kubica No Starch Press, Incorporated, 1, 2024
Graph Algorithms the Fun Way offers a refreshing approach to complex concepts by blending humor, imaginative examples, and practical Python implementations to reveal the power and versatility of graph-based problem-solving in the real world. Through clear diagrams, engaging examples, and Python code, you’ll build a solid foundation for addressing graph problems in your own projects. Explore a rich landscape of cleverly constructed scenarios where: Hedge mazes illuminate depth-first search Urban explorations demonstrate breadth-first search Intricate labyrinths reveal bridges and articulation points Strategic planning illustrates bipartite matching From fundamental graph structures to advanced topics, you will: Implement powerful algorithms, including Dijkstra’s, A*, and Floyd-Warshall Tackle puzzles and optimize pathfinding with newfound confidence Uncover real-world applications in social networks and transportation system Develop robust intuition for when and why to apply specific graph techniques Delve into topological sorting, minimum spanning trees, strongly connected components, and random walks. Confront challenges like graph coloring and the traveling salesperson problem. Prepare to view the world through the lens of graphs—where connections reveal insights and algorithms unlock new possibilities. Author Bio Jeremy Kubica is an engineering director working at the intersection of computer science and astrophysics. He holds a PhD in robotics from Carnegie Mellon University and a BS in computer science from Cornell University. He is the author of The CS Detective, Data Structures the Fun Way (both from No Starch Press), and Computational Fairy Tales.
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English [en] · PDF · 61.2MB · 2024 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/zlib · Save
base score: 11065.0, final score: 167585.0
34 partial matches
nexusstc/High-Performence Computing - Parallel, Distributed, and Cache-Conscious Algorithm Design and Analysis, Multithreaded Algorithms, Prefix Sums, Tree Contraction, Work-Efficient Parallel BFS, Graph Separation, Partitioning, Connectivity, Supercomputing/9e4a047614cd0ca4cb8c56eb808974c1.pdf
High-Performence Computing - Parallel, Distributed, and Cache-Conscious Algorithm Design and Analysis, Multithreaded Algorithms, Prefix Sums, Tree Contraction, Work-Efficient Parallel BFS, Graph Separation, Partitioning, Connectivity, Supercomputing Various
1 Introduction 2 Breadth-First Search Overview 2.1 Preliminaries 2.2 Parallel BFS: Prior Work 3 Breadth-First Search on Distributed Memory Systems 3.1 BFS with 1D Partitioning 3.2 BFS with 2D Partitioning 4 Implementation Details 4.1 Graph Representation 4.2 Local Computation 4.3 Distributed-memory parallelism 4.4 Load-balancing traversal 5 Algorithm Analysis 5.1 Analysis of the 1D Algorithm 5.2 Analysis of the 2D Algorithm 6 Experimental Studies 7 Conclusions and Future Work 8 References Introduction Background on BSP and Pregel Systems Tested Giraph GPS Mizan GraphLab Algorithms PageRank SSSP WCC DMST Evaluation Methodology System Setup Datasets Algorithms Evaluation Metrics Experimental Results Summary of Results Giraph GPS Mizan GraphLab Results for LJ and OR Datasets Experiences Giraph GPS Mizan GraphLab Conclusion References
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English [en] · PDF · 17.6MB · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save
base score: 11061.0, final score: 58.15141
nexusstc/Data Structures the Fun Way: An Amusing Adventure with Coffee-Filled Examples/af6b9dfc3b1a737924cff6d79d74aa02.pdf
Data Structures the Fun Way : An Amusing Adventure with Coffee-Filled Examples Jeremy Kubica No Starch Press, Incorporated, 1, 2022
**Learn how and when to use the right data structures in any situation, strengthening your computational thinking, problem-solving, and programming skills in the process.** This accessible and entertaining book provides an in-depth introduction to computational thinking through the lens of data structures -- a critical component in any programming endeavor. You'll learn how to work with more than 15 key data structures, from arrays, stacks, and queues, to caches, bloom filters, skip lists, and graphs. You'll also master linked lists by virtually standing in line at a cafe, hash tables by cataloging the history of the summer Olympics, and QuadTrees by neatly organizing your kitchen cabinets, all while becoming familiar with basic computer science concepts, like recursion and running time analysis.
Read more…
English [en] · PDF · 5.0MB · 2022 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save
base score: 11065.0, final score: 54.132584
upload/newsarch_ebooks/2022/08/25/Data Structures the Fun Way.epub
Data Structures the Fun Way : An Amusing Adventure with Coffee-Filled Examples Jeremy Kubica No Starch Press, Incorporated, Penguin Random House LLC (Publisher Services), New York, 2022
**Learn how and when to use the right data structures in any situation, strengthening your computational thinking, problem-solving, and programming skills in the process.** This accessible and entertaining book provides an in-depth introduction to computational thinking through the lens of data structures -- a critical component in any programming endeavor. You'll learn how to work with more than 15 key data structures, from arrays, stacks, and queues, to caches, bloom filters, skip lists, and graphs. You'll also master linked lists by virtually standing in line at a cafe, hash tables by cataloging the history of the summer Olympics, and QuadTrees by neatly organizing your kitchen cabinets, all while becoming familiar with basic computer science concepts, like recursion and running time analysis.
Read more…
English [en] · EPUB · 3.2MB · 2022 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save
base score: 11065.0, final score: 50.586395
upload/newsarch_ebooks/2023/07/19/Graph Algorithms for Data Science MEAP V08.epub
Graph Algorithms for Data Science MEAP V08 Tomaz Bratanic Manning Publications, 2023
Graphs are the natural way to understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with practical examples and concrete advice on implementation and deployment. In Graph Algorithms for Data Science you will learn: Labeled-property graph modeling Constructing a graph from structured data such as CSV or SQL NLP techniques to construct a graph from unstructured data Cypher query language syntax to manipulate data and extract insights Social network analysis algorithms like PageRank and community detection How to translate graph structure to a ML model input with node embedding models Using graph features in node classification and link prediction workflows Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis. It’s filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You’ll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. You don’t need any graph experience to start benefiting from this insightful guide. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects. about the technology Graphs reveal the relationships in your data. Tracking these interlinking connections reveals new insights and influences and lets you analyze each data point as part of a larger whole. This interconnected data is perfect for machine learning, as well as analyzing social networks, communities, and even product recommendations. about the book Graph Algorithms for Data Science teaches you how to construct graphs from both structured and unstructured data. You’ll learn how the flexible Cypher query language can be used to easily manipulate graph structures, and extract amazing insights. The book explores common and useful graph algorithms like PageRank and community detection/clustering algorithms. Each new algorithm you learn is instantly put into action to complete a hands-on data project, including modeling a social network! Finally, you’ll learn how to utilize graphs to upgrade your machine learning, including utilizing node embedding models and graph neural networks. about the reader For data scientists who know the basics of Machine Learning. Examples use the Cypher query language, which is explained in the book. about the author Tomaz Bratanic is a network scientist at heart, working at the intersection of graphs and machine learning. He has applied these graph techniques to projects in various domains including fraud detection, biomedicine, business-oriented analytics, and recommendations.
Read more…
English [en] · EPUB · 11.0MB · 2023 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save
base score: 11065.0, final score: 48.136166
nexusstc/Data Structures the Fun Way From Binary Search to QuadTrees in 100 Cups of Coffee/b578486f3575493b6da98c6491dca1f7.pdf
DATA STRUCTURES THE FUN WAY : from binary search to quadtrees in 100 cups of coffee Kubica, Jeremy No Starch Press, Incorporated, Early Access, 2022
Learn how and when to use the right data structures in any situation, strengthening your computational thinking, problem-solving, and programming skills in the process. This accessible and entertaining book provides an in-depth introduction to computational thinking through the lens of data structures — a critical component in any programming endeavor. You’ll learn how to work with more than 15 key data structures, from arrays, stacks, and queues, to caches, bloom filters, skip lists, and graphs. You’ll also master linked lists by virtually standing in line at a cafe, hash tables by cataloging the history of the summer Olympics, and QuadTrees by neatly organizing your kitchen cabinets, all while becoming familiar with basic computer science concepts, like recursion and running time analysis.
Read more…
English [en] · PDF · 11.0MB · 2022 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save
base score: 11065.0, final score: 48.095303
upload/newsarch_ebooks/2023/07/02/Graph Algorithms for Data Science.epub
Graph Algorithms for Data Science (MEAP v7) Tomaž Bratanič Manning Publications, Chaptes 1 to 11 of 12, 2023
Graphs are the natural way to understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with practical examples and concrete advice on implementation and deployment. In Graph Algorithms for Data Science you will learn Labeled-property graph modeling Constructing a graph from structured data such as CSV or SQL NLP techniques to construct a graph from unstructured data Cypher query language syntax to manipulate data and extract insights Social network analysis algorithms like PageRank and community detection How to translate graph structure to a ML model input with node embedding models Using graph features in node classification and link prediction workflows Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis. It’s filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You’ll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. You don’t need any graph experience to start benefiting from this insightful guide. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects. about the technology Graphs reveal the relationships in your data. Tracking these interlinking connections reveals new insights and influences and lets you analyze each data point as part of a larger whole. This interconnected data is perfect for machine learning, as well as analyzing social networks, communities, and even product recommendations. about the book Graph Algorithms for Data Science teaches you how to construct graphs from both structured and unstructured data. You’ll learn how the flexible Cypher query language can be used to easily manipulate graph structures, and extract amazing insights. The book explores common and useful graph algorithms like PageRank and community detection/clustering algorithms. Each new algorithm you learn is instantly put into action to complete a hands-on data project, including modeling a social network! Finally, you’ll learn how to utilize graphs to upgrade your machine learning, including utilizing node embedding models and graph neural networks. about the reader For data scientists who know the basics of machine learning. Examples use the Cypher query language, which is explained in the book. about the author Tomaž Bratanič is a network scientist at heart, working at the intersection of graphs and machine learning. He has applied these graph techniques to projects in various domains including fraud detection, biomedicine, business-oriented analytics, and recommendations.
Read more…
English [en] · EPUB · 10.0MB · 2023 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save
base score: 11065.0, final score: 48.005394
nexusstc/Data Structures the Fun Way: An Amusing Adventure with Coffee-Filled Examples/dae2cdae23fdd2d014a2962d42e4c8c3.azw
Data Structures the Fun Way : An Amusing Adventure with Coffee-Filled Examples Jeremy Kubica No Starch Press, Incorporated, Penguin Random House LLC (Publisher Services), New York, 2022
**Learn how and when to use the right data structures in any situation, strengthening your computational thinking, problem-solving, and programming skills in the process.** This accessible and entertaining book provides an in-depth introduction to computational thinking through the lens of data structures -- a critical component in any programming endeavor. You'll learn how to work with more than 15 key data structures, from arrays, stacks, and queues, to caches, bloom filters, skip lists, and graphs. You'll also master linked lists by virtually standing in line at a cafe, hash tables by cataloging the history of the summer Olympics, and QuadTrees by neatly organizing your kitchen cabinets, all while becoming familiar with basic computer science concepts, like recursion and running time analysis.
Read more…
English [en] · AZW · 2.2MB · 2022 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save
base score: 11050.0, final score: 47.97137
nexusstc/Data Structures the Fun Way: An Amusing Adventure with Coffee-Filled Examples/89191dce6a3c49a8efcf5247bfc9527e.epub
Data Structures the Fun Way : An Amusing Adventure with Coffee-Filled Examples Jeremy Kubica No Starch Press, Incorporated, Penguin Random House LLC (Publisher Services), New York, 2022
**Learn how and when to use the right data structures in any situation, strengthening your computational thinking, problem-solving, and programming skills in the process.** This accessible and entertaining book provides an in-depth introduction to computational thinking through the lens of data structures -- a critical component in any programming endeavor. You'll learn how to work with more than 15 key data structures, from arrays, stacks, and queues, to caches, bloom filters, skip lists, and graphs. You'll also master linked lists by virtually standing in line at a cafe, hash tables by cataloging the history of the summer Olympics, and QuadTrees by neatly organizing your kitchen cabinets, all while becoming familiar with basic computer science concepts, like recursion and running time analysis.
Read more…
English [en] · EPUB · 3.2MB · 2022 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save
base score: 11065.0, final score: 47.75917
nexusstc/Algorithms for Graph Similarity and Subgraph Matching/6231599be5583c0b6a4df8d48078afd8.pdf
Algorithms for Graph Similarity and Subgraph Matching Danai Koutra, Ankur Parikh, Aaditya Ramdas, Aaditya Ramdas
English [en] · PDF · 2.7MB · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save
base score: 11058.0, final score: 46.917007
nexusstc/Handbook of Graph Drawing and Visualization: Draft of 2013 edition/d6e78ba7a942e1219d1be5b603984547.pdf
Handbook of Graph Drawing and Visualization: Draft of 2013 edition Tamassia R. (Ed.) 2013
CRC Press, 2014. — 860 p. Get an In-Depth Understanding of Graph Drawing Techniques, Algorithms, Software, and Applications The Handbook of Graph Drawing and Visualization provides a broad, up-to-date survey of the field of graph drawing. It covers topological and geometric foundations, algorithms, software systems, and visualization applications in business, education, science, and engineering. Each chapter is self-contained and includes extensive references. The first several chapters of the book deal with fundamental topological and geometric concepts and techniques used in graph drawing, such as planarity testing and embedding, crossings and planarization, symmetric drawings, and proximity drawings. The following chapters present a large collection of algorithms for constructing drawings of graphs, including tree, planar straight-line, planar orthogonal and polyline, spine and radial, circular, rectangular, hierarchical, and three-dimensional drawings as well as labeling algorithms, simultaneous embeddings, and force-directed methods. The book then introduces the GraphML language for representing graphs and their drawings and describes three software systems for constructing drawings of graphs: OGDF, GDToolkit, and PIGALE. The final chapters illustrate the use of graph drawing methods in visualization applications for biological networks, computer security, data analytics, education, computer networks, and social networks. Edited by a pioneer in graph drawing and with contributions from leaders in the graph drawing research community, this handbook shows how graph drawing and visualization can be applied in the physical, life, and social sciences. Whether you are a mathematics researcher, IT practitioner, or software developer, the book will help you understand graph drawing methods and graph visualization systems, use graph drawing techniques in your research, and incorporate graph drawing solutions in your products.
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English [en] · PDF · 35.9MB · 2013 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save
base score: 11063.0, final score: 46.812008
nexusstc/Graph and Network Algorithms [Lecture notes]/23277bf3605ad0208149a29a89848aee.pdf
Graph and Network Algorithms [Lecture notes] Christopher Griffin 2016
English [en] · PDF · 4.0MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save
base score: 11060.0, final score: 46.61923
lgli/Loiane Groner - Learning JavaScript Data Structures and Algorithms: Write complex and powerful JavaScript code using the latest ECMAScript, 3rd Edition (2018, Packt Publishing).epub
Learning JavaScript Data Structures and Algorithms: Write complex and powerful JavaScript code using the latest ECMAScript, 3rd Edition Groner, Loiane Packt Publishing, 2018
A data structure is a particular way of organizing data in a computer to utilize resources efficiently. Data structures and algorithms are the base of every solution to any programming problem. With this book, you will learn to write complex and powerful code using the latest ES 8 features.
Read more…
English [en] · EPUB · 3.0MB · 2018 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save
base score: 11068.0, final score: 46.30161
lgli/Loiane Groner - Learning JavaScript Data Structures and Algorithms: Write complex and powerful JavaScript code using the latest ECMAScript, 3rd Edition (2018, Packt Publishing).epub
Learning JavaScript Data Structures and Algorithms: Write complex and powerful JavaScript code using the latest ECMAScript, 3rd Edition Groner, Loiane Packt Publishing, 2018
A data structure is a particular way of organizing data in a computer to utilize resources efficiently. Data structures and algorithms are the base of every solution to any programming problem. With this book, you will learn to write complex and powerful code using the latest ES 8 features.
Read more…
English [en] · EPUB · 3.0MB · 2018 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save
base score: 11068.0, final score: 46.30161
lgli/Loiane Groner - Learning JavaScript Data Structures and Algorithms: Write complex and powerful JavaScript code using the latest ECMAScript, 3rd Edition (2018, Packt Publishing).azw3
Learning JavaScript Data Structures and Algorithms: Write complex and powerful JavaScript code using the latest ECMAScript, 3rd Edition Loiane Groner Packt Publishing, 2018
A data structure is a particular way of organizing data in a computer to utilize resources efficiently. Data structures and algorithms are the base of every solution to any programming problem. With this book, you will learn to write complex and powerful code using the latest ES 8 features.
Read more…
English [en] · AZW3 · 10.1MB · 2018 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save
base score: 11058.0, final score: 46.286404
lgli/Loiane Groner - Learning JavaScript Data Structures and Algorithms: Write complex and powerful JavaScript code using the latest ECMAScript, 3rd Edition (2018, Packt Publishing).pdf
Learning JavaScript Data Structures and Algorithms: Write complex and powerful JavaScript code using the latest ECMAScript, 3rd Edition Loiane Groner Packt Publishing, 2018
A data structure is a particular way of organizing data in a computer to utilize resources efficiently. Data structures and algorithms are the base of every solution to any programming problem. With this book, you will learn to write complex and powerful code using the latest ES 8 features.
Read more…
English [en] · PDF · 6.6MB · 2018 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save
base score: 11068.0, final score: 46.28384
lgli/Loiane Groner - Learning JavaScript Data Structures and Algorithms: Write complex and powerful JavaScript code using the latest ECMAScript, 3rd Edition (2018, Packt Publishing).pdf
Learning JavaScript Data Structures and Algorithms: Write complex and powerful JavaScript code using the latest ECMAScript, 3rd Edition Loiane Groner Packt Publishing, 3, 2018
A data structure is a particular way of organizing data in a computer to utilize resources efficiently. Data structures and algorithms are the base of every solution to any programming problem. With this book, you will learn to write complex and powerful code using the latest ES 8 features.
Read more…
English [en] · PDF · 6.6MB · 2018 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save
base score: 11068.0, final score: 46.275753
nexusstc/Graph algorithms and applications 5/b977151ae10776b63c6f5bf39013ff1c.pdf
Graph algorithms and applications 5 Giuseppe Liotta; Roberto Tamassia; Ioannis G Tollis; ProQuest (Firm) World Scientific Publishing Company, World Scientific Publishing Company, Singapore, 2006
This book contains Volume 7 of the "Journal of Graph Algorithms and Applications" (JGAA). JGAA is a peer-reviewed scientific journal devoted to the publication of high-quality research papers on the analysis, design, implementation, and applications of graph algorithms. Areas of interest include computational biology, computational geometry, computer graphics, computer-aided design, computer and interconnection networks, constraint systems, databases, graph drawing, graph embedding and layout, knowledge representation, multimedia, software engineering, telecommunications networks, user interfaces and visualization, and VLSI circuit design. "Graph Algorithms and Applications 4" presents contributions from prominent authors and includes selected papers from the Seventh International Workshop on Algorithms and Data Structures (WADS 2001) and the 2001 Symposium on Graph Drawing (GD 2001). All papers in the book have extensive diagrams and offer a unique treatment of graph algorithms focusing on the important applications.
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English [en] · PDF · 7.2MB · 2006 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save
base score: 11065.0, final score: 46.069466
lgli/Rubik Cube - XNXNXNXN Cube Algorithms - Rubik’s Cube Solution PDF 1(2022, Zack Daniel Cube Algorithms Game).pdf
XNXNXNXN Cube Algorithms - Rubik’s Cube Solution PDF Rubik Cube Zack Daniel Cube Algorithms Game, Rubik’s Revenge Solution Hints Booklet, 1, 1, 2022
auto;text-align:center;line-height:normal;mso-outline-level:2" align="center">"Times New Roman";mso-fareast-language:FR">xnxnxnxn Cube Algorithms PDFline-height:normal">mso-fareast-font-family:"Times New Roman";mso-fareast-language:FR">There aresome other Rubik’s cubes in the Rubik’s cube family along with the Rubik’s Cube(1974), Rubik’s Magic, Rubik’s Magic: Master Edition, and Rubik’s Snake. Inthis post, you can download the xnxnxnxn Cube Algorithms PDF with oneclick by using the link below. RUBIK’S Revenge has 6 faces. Each face will be asingle solid color when the puzzle is solved. The puzzle has 12 differentlayers and each can be tuned independently of the othersline-height:normal">mso-fareast-font-family:"Times New Roman";mso-fareast-language:FR">Here you caneasily download xnxnxnxn Cube Algorithms PDF for free/ Rubik’s CubeRevenge Parity Algorithms PDF (Solution Guide)from the download button which is given in the below table.
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English [en] · PDF · 0.2MB · 2022 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save
base score: 11058.0, final score: 45.86083
ia/graphpartitionin0000dima.pdf
Graph partitioning and graph clustering : 10th DIMACS Implementation Challenge Workshop, February 13-14, 2012, Georgia Institute of Technology, Atlanta, GA David A. Bader, Henning Meyerhenke, Peter Sanders, Dorothea Wagner, editors American Mathematical Society ; Center for Discrete Mathematics and Theoretical Computer Science, Contemporary mathematics (American Mathematical Society), Providence, Rhode Island, 2013
Graph Partitioning And Graph Clustering Are Ubiquitous Subtasks In Many Applications Where Graphs Play An Important Role. Generally Speaking, Both Techniques Aim At The Identification Of Vertex Subsets With Many Internal And Few External Edges. To Name Only A Few, Problems Addressed By Graph Partitioning And Graph Clustering Algorithms Are: What Are The Communities Within An (online) Social Network? ; How Do I Speed Up A Numerical Simulation By Mapping It Efficiently Onto A Parallel Computer? ; How Must Components Be Organized On A Computer Chip Such That They Can Communicate Efficiently With Each Other? ; What Are The Segments Of A Digital Image? ; Which Functions Are Certain Genes (most Likely) Responsible For?. The 10th Dimacs Implementation Challenge Workshop Was Devoted To Determining Realistic Performance Of Algorithms Where Worst Case Analysis Is Overly Pessimistic And Probabilistic Models Are Too Unrealistic. Articles In The Volume Describe And Analyze Various Experimental Data With The Goal Of Getting Insight Into Realistic Algorithm Performance In Situations Where Analysis Fails. This Book Is Published In Cooperation With The Center For Discrete Mathematics And Theoretical Computer Science.--publisher's Website. Preface / David A. Bader, Henning Meyerhenke, Peter Sanders, And Dorothea Wagner -- High Quality Graph Partitioning / Peter Sanders And Christian Schulz -- Abusing A Hypergraph Partitioner For Unweighted Graph Partitioning / B.o. Fagginger Auer And R.h. Bisseling -- Parallel Partitioning With Zoltan: Is Hypergraph Partitioning Worth It? / Sivasankaran Rajamanickam And Erik G. Boman -- Umpa: A Multi-objective, Multi-level Partitioner For Communication Minimization / Ümit V. Çatalyürek, Mehmet Deveci, Kamer Kaya, And Bora Uçar -- Shape Optimizing Load Balancing For Mpi-parallel Adaptive Numerical Simulations / Henning Meyerhenke -- Graph Partitioning For Scalable Distributed Graph Computations / Aydin Buluç And Kamesh Madduri -- Using Graph Partitioning For Efficient Network Modularity Optimization / Hristo Djidjev And Melih Onus -- Modularity Maximization In Networks By Variable Neighborhood Search / Daniel Aloise, Gilles Caporossi, Pierre Hansen, Leo Liberti, Sylvain Perron, And Manuel Ruiz -- Network Clustering Via Clique Relaxations: A Community Based Approach / Anurag Verma And Sergiy Butenko -- Identifying Base Clusters And Their Application To Maximizing Modularity / Sriram Srinivasan, Tanmoy Chakraborty, And Sanjukta Bhowmick -- Complete Hierarchical Cut-clustering: A Case Study On Expansion And Modularity / Michael Hamann, Tanja Hartmann, And Dorothea Wagner -- A Partitioning-based Divisive Clustering Technique For Maximizing The Modularity / Ümit V. Çatalyürek, Kamer Kaya, Johannes Langguth, And Bora Uçar -- An Ensemble Learning Strategy For Graph Clustering / Michael Ovelgönne And Andreas Geyer-schulz -- Parallel Community Detection For Massive Graphs / E. Jason Riedy, Henning Meyerhenke, David Ediger, And David A. Bader -- Graph Coarsening And Clustering On The Gpu / B.o. Fagginger Auer And R.h. Bisseling. David A. Bader, Henning Meyerhenke, Peter Sanders, Dorothea Wagner, Editors. Includes Bibliographical References.
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English [en] · PDF · 18.4MB · 2013 · 📗 Book (unknown) · 🚀/ia · Save
base score: 11068.0, final score: 45.854103
duxiu/initial_release/g/data/duxiu_and_related/DX6.0/zips/14783034_算法详解 卷2 图算法和数据结构=ALGORITHMS ILLUMINATED PART 2: GRAPH ALGORITHMS AND DATA STRUCTURES.zip
算法详解 卷2 图算法和数据结构=ALGORITHMS ILLUMINATED PART 2: GRAPH ALGORITHMS AND DATA STRUCTURES (美) 蒂姆拉夫加登(Tim Roughgarden)著,徐波译 北京人民邮电出版社, 2020.6
1 (p1): 第1章 图的基础知识 1 (p1-1): 1.1 基本术语 2 (p1-2): 1.2 图的一些应用 3 (p1-3): 1.3 图形的度量 3 (p1-3-1): 1.3.1 图的边数量 4 (p1-3-2): 1.3.2 稀疏图和稠密图 5 (p1-3-3): 1.3.3 小测验1.1 的答案 7 (p1-4): 1.4 图的表示方法 7 (p1-4-1): 1.4.1 邻接列表 8 (p1-4-2): 1.4.2 邻接矩阵 9 (p1-4-3): 1.4.3 图的表示形式之间的比较 10 (p1-4-4): 1.4.4 小测验1.2 和小测验1.3 的答案 11 (p1-5): 1.5 本章要点 12 (p1-6): 1.6 章末习题 14 (p2): 第2章 图的搜索及其应用 14 (p2-1): 2.1 概述 15 (p2-1-1): 2.1.1 一些应用 16 (p2-1-2): 2.1.2 零代价的基本算法 17 (p2-1-3): 2.1.3 通用的图搜索算法 20 (p2-1-4): 2.1.4 宽度优先的搜索和深度优先的搜索 22 (p2-1-5): 2.1.5 GenericSearch算法的正确性 23 (p2-2): 2.2 宽度优先的搜索和最短路径 23 (p2-2-1): 2.2.1 高层思路 24 (p2-2-2): 2.2.2 BFS的伪码 25 (p2-2-3): 2.2.3 BFS的一个例子 27 (p2-2-4): 2.2.4 正确性和运行时间 28 (p2-2-5): 2.2.5 最短路径 31 (p2-2-6): 2.2.6 小测验2.1 的答案 32 (p2-3): 2.3 计算连通分量 32 (p2-3-1): 2.3.1 连通分量 33 (p2-3-2): 2.3.2 连通分量的应用 34 (p2-3-3): 2.3.3 UCC(无向图连通分量)算法 35 (p2-3-4): 2.3.4 UCC算法的一个例子 36 (p2-3-5): 2.3.5 UCC算法的正确性和运行时间 37 (p2-3-6): 2.3.6 小测验2.2 的答案 37 (p2-4): 2.4 深度优先的搜索 37 (p2-4-1): 2.4.1 DFS的一个例子 39 (p2-4-2): 2.4.2 DFS的伪码 41 (p2-4-3): 2.4.3 正确性和运行时间 41 (p2-5): 2.5 拓扑排序 41 (p2-5-1): 2.5.1 拓扑顺序 43 (p2-5-2): 2.5.2 什么时候存在拓扑顺序 45 (p2-5-3): 2.5.3 计算拓扑顺序 46 (p2-5-4): 2.5.4 通过DFS的拓扑排序 47 (p2-5-5): 2.5.5 拓扑排序的一个例子 48 (p2-5-6): 2.5.6 正确性和运行时间 49 (p2-5-7): 2.5.7 小测验2.3 和小测验2.4 的答案 50 (p2-6): 2.6 计算强连通分量 50 (p2-6-1): 2.6.1 强连通分量的定义 52 (p2-6-2): 2.6.2 为什么要使用深度优先的搜索 53 (p2-6-3): 2.6.3 为什么要使用反转的图 57 (p2-6-4): 2.6.4 Kosaraju的伪码 59 (p2-6-5): 2.6.5 一个例子 60 (p2-6-6): 2.6.6 正确性和运行时间 60 (p2-6-7): 2.6.7 小测验2.5 和小测验2.6 的答案 61 (p2-7): 2.7 Web的结构 62 (p2-7-1): 2.7.1 Web图 63 (p2-7-2): 2.7.2 蝴蝶结 64 (p2-7-3): 2.7.3 主要发现 65 (p2-8): 2.8 本章要点 65 (p2-9): 2.9 章末习题 70 (p3): 第3章 Dijkstra最短路径算法 70 (p3-1): 3.1 单源最短路径问题 70 (p3-1-1): 3.1.1 问题定义 72 (p3-1-2): 3.1.2 一些前提条件 72 (p3-1-3): 3.1.3 为什么不使用宽度优先的搜索 73 (p3-1-4): 3.1.4 小测验3.1 的答案 74 (p3-2): 3.2 Dijkstra算法 74 (p3-2-1): 3.2.1 伪码 76 (p3-2-2): 3.2.2 一个例子 77 (p3-3): 3.3 为什么Dijkstra算法是正确的 77 (p3-3-1): 3.3.1 一种虚假的简化 78 (p3-3-2): 3.3.2 Dijkstra算法的一个糟糕例子 78 (p3-3-3): 3.3.3 非负边长时的正确性 82 (p3-4): 3.4 算法的实现及其运行时间 84 (p3-5): 3.5 本章要点 84 (p3-6): 3.6 章末习题 88 (p4): 第4章 堆数据结构 88 (p4-1): 4.1 数据结构概述 88 (p4-1-1): 4.1.1 选择正确的数据结构 89 (p4-1-2): 4.1.2 进入更高层次 90 (p4-2): 4.2 堆所支持的操作 91 (p4-2-1): 4.2.1 Insert和ExtractMin 92 (p4-2-2): 4.2.2 其他操作 93 (p4-3): 4.3 堆的应用 93 (p4-3-1): 4.3.1 应用:排序 96 (p4-3-2): 4.3.2 应用:事件管理器 96 (p4-3-3): 4.3.3 应用:中位值维护 98 (p4-4): 4.4 Dijkstra算法的提速 98 (p4-4-1): 4.4.1 为什么要使用堆 99 (p4-4-2): 4.4.2 计划 101 (p4-4-3): 4.4.3 维持不变性 103 (p4-4-4): 4.4.4 运行时间 104 (p4-5): 4.5 实现细节 104 (p4-5-1): 4.5.1 树形式的堆 106 (p4-5-2): 4.5.2 数组形式的堆 107 (p4-5-3): 4.5.3 在O(log n)时间内实现Insert操作 111 (p4-5-4): 4.5.4 在O(log n)时间内实现ExtractMin操作 114 (p4-6): 4.6 本章要点 114 (p4-7): 4.7 章末习题 117 (p5): 第5章 搜索树 117 (p5-1): 5.1 有序数组 117 (p5-1-1): 5.1.1 有序数组支持的操作 119 (p5-1-2): 5.1.2 有序数组不支持的操作 120 (p5-2): 5.2 搜索树支持的操作 122 (p5-3): 5.3 实现细节 122 (p5-3-1): 5.3.1 搜索树的属性 123 (p5-3-2): 5.3.2 搜索树的高度 124 (p5-3-3): 5.3.3 在O(高度)时间内实现Search 125 (p5-3-4): 5.3.4 在O(高度)时间内实现Min和Max 126 (p5-3-5): 5.3.5 在O(高度)时间内实现Predecessor 127 (p5-3-6): 5.3.6 在O(n)时间内实现OutputSorted操作 128 (p5-3-7): 5.3.7 在O(高度)时间内实现Insert操作 129 (p5-3-8): 5.3.8 在O(高度)时间内实现Delete操作 132 (p5-3-9): 5.3.9 强化的搜索树支持Select操作 134 (p5-3-10): 5.3.10 小测验5.1 的答案 134 (p5-4): 5.4 平衡搜索树 134 (p5-4-1): 5.4.1 努力实现更好的平衡 135 (p5-4-2): 5.4.2 旋转 137 (p5-5): 5.5 本章要点 138 (p5-6): 5.6 章末习题 140 (p6): 第6章 散列表和布隆过滤器 140 (p6-1): 6.1 支持的操作 143 (p6-2): 6.2 散列表的应用 144 (p6-2-1): 6.2.1 应用:消除重复 145 (p6-2-2): 6.2.2 应用:两数之和问题 147 (p6-2-3): 6.2.3 应用:搜索巨大的状态空间 148 (p6-2-4): 6.2.4 小测验6.2 的答案 148 (p6-3): 6.3 实现的高层思路 148 (p6-3-1): 6.3.1 两个简单的解决方案 149 (p6-3-2): 6.3.2 散列函数 150 (p6-3-3): 6.3.3 冲突是不可避免的 152 (p6-3-4): 6.3.4 解决冲突的方法:链地址法 153 (p6-3-5): 6.3.5 解决冲突的方法:开放地址法 156 (p6-3-6): 6.3.6 良好的散列函数是怎么样的 160 (p6-3-7): 6.3.7 小测验6.3 至小测验6.5 的答案 162 (p6-4): 6.4 更多的实现细节 162 (p6-4-1): 6.4.1 负载和性能 164 (p6-4-2): 6.4.2 管理散列表的负载 165 (p6-4-3): 6.4.3 选择散列函数 166 (p6-4-4): 6.4.4 选择冲突解决策略 166 (p6-4-5): 6.4.5 小测验6.6 的答案 166 (p6-5): 6.5 布隆过滤器的基础知识 167 (p6-5-1): 6.5.1 布隆过滤器支持的操作 169 (p6-5-2): 6.5.2 布隆过滤器的应用 169 (p6-5-3): 6.5.3 布隆过滤器的实现 172 (p6-6): 6.6 布隆过滤器的启发式分析 172 (p6-6-1): 6.6.1 启发式假设 174 (p6-6-2): 6.6.2 部分位被设置为1 175 (p6-6-3): 6.6.3 假阳性率 176 (p6-6-4): 6.6.4 结束语 177 (p6-6-5): 6.6.5 小测验6.7 的答案 178 (p6-7): 6.7 本章要点 179 (p6-8): 6.8 章末习题 181 (p7): 附录 快速回顾渐进性表示法 187 (p8): 部分习题答案
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Chinese [zh] · PDF · 33.2MB · 2019 · 📗 Book (unknown) · 🚀/duxiu · Save
base score: 11060.0, final score: 45.84945
nexusstc/Guide to Graph Algorithms Sequential, Parallel and Distributed/ab5a720fe0641c4c55c0ba39f2460a93.epub
Guide to Graph Algorithms: Sequential, Parallel and Distributed (Texts in Computer Science) Kayhan Erciyes Springer International Publishing : Imprint : Springer, Texts in computer science, Cham, Switzerland, 2018
This clearly structured textbook/reference presents a detailed and comprehensive review of the fundamental principles of sequential graph algorithms, approaches for NP-hard graph problems, and approximation algorithms and heuristics for such problems. The work also provides a comparative analysis of sequential, parallel and distributed graph algorithms – including algorithms for big data – and an investigation into the conversion principles between the three algorithmic methods. Topics and features: Presents a comprehensive analysis of sequential graph algorithms Offers a unifying view by examining the same graph problem from each of the three paradigms of sequential, parallel and distributed algorithms Describes methods for the conversion between sequential, parallel and distributed graph algorithms Surveys methods for the analysis of large graphs and complex network applications Includes full implementation details for the problems presented throughout the text Provides additional supporting material at an accompanying website This practical guide to the design and analysis of graph algorithms is ideal for advanced and graduate students of computer science, electrical and electronic engineering, and bioinformatics. The material covered will also be of value to any researcher familiar with the basics of discrete mathematics, graph theory and algorithms. Dr. K. Erciyes is an emeritus professor of computer engineering at Ege University, Turkey. His other publications include the Springer titles Distributed Graph Algorithms for Computer Networks and Distributed and Sequential Algorithms for Bioinformatics.
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English [en] · EPUB · 7.0MB · 2018 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save
base score: 11065.0, final score: 45.711216
lgli/Data Structures the Fun Way - Jeremy Kubica;.epub
Data Structures the Fun Way : An Amusing Adventure with Coffee-Filled Examples Jeremy Kubica No Starch Press, Incorporated, Penguin Random House LLC (Publisher Services), New York, 2022
"Learn how and when to use the right data structures in any situation, strengthening your computational thinking, problem-solving, and programming skills in the process. This accessible and entertaining book provides an in-depth introduction to computational thinking through the lens of data structures -- a critical component in any programming endeavor. You'll learn how to work with more than 15 key data structures, from stacks, queues, and caches to bloom filters, skip lists, and graphs. You'll also master linked lists by virtually standing in line at a cafe, hash tables by cataloging the history of the summer Olympics, and Quadtrees by neatly organizing your kitchen cabinets, all while becoming familiar with basic computer science concepts, like recursion and running time analysis."-- Provided by publisher
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English [en] · EPUB · 3.2MB · 2022 · 📘 Book (non-fiction) · 🚀/lgli/lgrs · Save
base score: 11065.0, final score: 45.617
nexusstc/Hands-On Graph Neural Networks Using Python: Practical techniques and architectures/be12ac19810306fa2043558635436763.epub
Hands-On Graph Neural Networks Using Python : Practical Techniques and Architectures for Building Powerful Graph and Deep Learning Apps with PyTorch Maxime Labonne Packt Publishing Pvt Ltd, 1, 2023
Design robust graph neural networks with PyTorch Geometric by combining graph theory and neural networks with the latest developments and apps Purchase of the print or Kindle book includes a free PDF eBook Key Features: Implement state-of-the-art graph neural network architectures in Python Create your own graph datasets from tabular data Build powerful traffic forecasting, recommender systems, and anomaly detection applications Book Description: Graph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as social networks, chemical compounds, or transportation networks. The past few years have seen an explosion in the use of graph neural networks, with their application ranging from natural language processing and computer vision to recommendation systems and drug discovery. Hands-On Graph Neural Networks Using Python begins with the fundamentals of graph theory and shows you how to create graph datasets from tabular data. As you advance, you'll explore major graph neural network architectures and learn essential concepts such as graph convolution, self-attention, link prediction, and heterogeneous graphs. Finally, the book proposes applications to solve real-life problems, enabling you to build a professional portfolio. The code is readily available online and can be easily adapted to other datasets and apps. By the end of this book, you'll have learned to create graph datasets, implement graph neural networks using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training graph neural network models for node and graph classification, link prediction, and much more. What You Will Learn: Understand the fundamental concepts of graph neural networks Implement graph neural networks using Python and PyTorch Geometric Classify nodes, graphs, and edges using millions of samples Predict and generate realistic graph topologies Combine heterogeneous sources to improve performance Forecast future events using topological information Apply graph neural networks to solve real-world problems Who this book is for: This book is for machine learning practitioners and data scientists interested in learning about graph neural networks and their applications, as well as students looking for a comprehensive reference on this rapidly growing field. Whether you're new to graph neural networks or looking to take your knowledge to the next level, this book has something for you. Basic knowledge of machine learning and Python programming will help you get the most out of this book.
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English [en] · EPUB · 15.8MB · 2023 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save
base score: 11065.0, final score: 45.527126
nexusstc/GRAPH COLORING ALGORITHMS††This research was supported in part by the Advanced Research Projects Agency of the Department of Defense under contract SD-302 and by the National Science Foundation under contract GJ-446./e5c7e2c42bc5adfb6aefa4c02ea5f054.pdf
GRAPH COLORING ALGORITHMS††This research was supported in part by the Advanced Research Projects Agency of the Department of Defense under contract SD-302 and by the National Science Foundation under contract GJ-446. David W. Matula; George Marble; Joel D. Isaacson Elsevier, Graph Theory and Computing, 1972
English [en] · PDF · 1.0MB · 1972 · 🤨 Other · nexusstc/scihub · Save
base score: 10979.0, final score: 45.422024
lgli/Cs_Computer science/CsLn_Lecture notes/A/Algorithms and Models for the Web-Graph.. 3 conf., WAW 2004(LNCS3243, Springer, 2004)(ISBN 3540234276)(201s).pdf
Algorithms and models for the web-graph: third international workshop, WAW 2004, Rome, Italy, October 16, 2004 ; proceeedings [i.e. proceedings] Béla Bollobás, Oliver Riordan (auth.), Stefano Leonardi (eds.) Springer-Verlag Berlin Heidelberg, Lecture Notes in Computer Science, Lecture Notes in Computer Science 3243, 1, 2004
This Volume Contains The 14 Contributed Papers And The Contribution Of The Distinguished Invited Speaker B´ Ela Bollob´ As Presented At The 3rd Workshop On Algorithms And Models For The Web-graph (waw 2004), Held In Rome, Italy, October 16, 2004, In Conjunction With The 45th Annual Ieee Symposium On Foundations Of Computer Science (focs 2004). The World Wide Web Has Become Part Of Our Everyday Life And Information Retrievalanddataminingonthewebisnowofenormouspracticalinterest.some Of The Algorithms Supporting These Activities Are Based Substantially On Viewing The Web As A Graph, Induced In Various Ways By Links Among Pages, Links Among Hosts, Or Other Similar Networks. Theaimofthe2004workshoponalgorithmsandmodelsfortheweb-graph Was To Further The Understanding Of These Web-induced Graphs, And Stimulate The Development Of High-performance Algorithms And Applications That Use The Graphstructureoftheweb.theworkshopwasmeantbothtofosteranexchange Of Ideas Among The Diverse Set Of Researchers Already Involved In This Topic, And To Act As An Introduction For The Larger Community To The State Of The Art In This Area. This Was The Third Edition Of A Very Successful Workshop On This Topic, Waw 2002 Was Held In Vancouver, Canada, In Conjunction With The 43rd - Nual Ieee Symposium On Foundations Of Computer Science, Focs 2002, And Waw 2003 Was Held In Budapest, Hungary, In Conjunction With The 12th Int- National World Wide Web Conference, Www 2003. This Was The ?rst Edition Of The Workshop With Formal Proceedings. Ibm Invited Lecture -- The Phase Transition And Connectedness In Uniformly Grown Random Graphs -- Contributed Papers -- Analyzing The Small World Phenomenon Using A Hybrid Model With Local Network Flow (extended Abstract) -- Dominating Sets In Web Graphs -- A Geometric Preferential Attachment Model Of Networks -- Traffic-driven Model Of The World Wide Web Graph -- On Reshaping Of Clustering Coefficients In Degree-based Topology Generators -- Generating Web Graphs With Embedded Communities -- Making Eigenvector-based Reputation Systems Robust To Collusion -- Towards Scaling Fully Personalized Pagerank -- Fast Pagerank Computation Via A Sparse Linear System (extended Abstract) -- T-rank: Time-aware Authority Ranking -- Links In Hierarchical Information Networks -- Crawling The Infinite Web: Five Levels Are Enough -- Do Your Worst To Make The Best: Paradoxical Effects In Pagerank Incremental Computations -- Communities Detection In Large Networks. Stefano Leonardi (ed.). This Volume Contains ... Papers ... Presented At The 3rd Workshop On Algorithms And Models For The Web-graph (waw 2004) ... In Conjunction With The 45th Annual Ieee Symposium On Foundations Of Computer Science (focs 2004).--pref. Includes Bibliographical References And Index. Also Issued Online.
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English [en] · PDF · 6.1MB · 2004 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/scihub/zlib · Save
base score: 11065.0, final score: 45.416203
nexusstc/Guide to Graph Algorithms Sequential, Parallel and Distributed/fc46a947529f536c2c5befc32fd2a647.pdf
Guide to Graph Algorithms: Sequential, Parallel and Distributed (Texts in Computer Science) Kayhan Erciyes Springer International Publishing : Imprint : Springer, Texts in computer science, Cham, Switzerland, 2018
This clearly structured textbook/reference presents a detailed and comprehensive review of the fundamental principles of sequential graph algorithms, approaches for NP-hard graph problems, and approximation algorithms and heuristics for such problems. The work also provides a comparative analysis of sequential, parallel and distributed graph algorithms – including algorithms for big data – and an investigation into the conversion principles between the three algorithmic methods. Topics and features: Presents a comprehensive analysis of sequential graph algorithms Offers a unifying view by examining the same graph problem from each of the three paradigms of sequential, parallel and distributed algorithms Describes methods for the conversion between sequential, parallel and distributed graph algorithms Surveys methods for the analysis of large graphs and complex network applications Includes full implementation details for the problems presented throughout the text Provides additional supporting material at an accompanying website This practical guide to the design and analysis of graph algorithms is ideal for advanced and graduate students of computer science, electrical and electronic engineering, and bioinformatics. The material covered will also be of value to any researcher familiar with the basics of discrete mathematics, graph theory and algorithms. Dr. K. Erciyes is an emeritus professor of computer engineering at Ege University, Turkey. His other publications include the Springer titles Distributed Graph Algorithms for Computer Networks and Distributed and Sequential Algorithms for Bioinformatics.
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English [en] · PDF · 11.2MB · 2018 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/scihub/zlib · Save
base score: 11065.0, final score: 45.2414
lgli/K:\!genesis\0day\kolxoz\79\M_Mathematics\MA_Algebra\MAc_Combinatorics\Thulasiraman K., et al. (eds.) Handbook of graph theory, combinatorial optimization, and algorithms (CRC, 2016)(ISBN 9781420011074)(O)(1213s)_MAc_.pdf
Handbook of graph theory, combinatorial optimization, and algorithms Krishnaiyan “KT” Thulasiraman (Editor), Subramanian Arumugam (Editor), Andreas Brandstädt (Editor), Takao Nishizeki (Editor) CRC Press LLC, Chapman & Hall/CRC computer and information science series, 1st, Boca Raton, Florida, 2016
The fusion between graph theory and combinatorial optimization has led to theoretically profound and practically useful algorithms, yet there is no book that currently covers both areas together. Handbook of Graph Theory, Combinatorial Optimization, and Algorithms is the first to present a unified, comprehensive treatment of both graph theory and combinatorial optimization. Divided into 11 cohesive sections, the handbook’s 44 chapters focus on graph theory, combinatorial optimization, and algorithmic issues. The book provides readers with the algorithmic and theoretical foundations to: • Understand phenomena as shaped by their graph structures • Develop needed algorithmic and optimization tools for the study of graph structures • Design and plan graph structures that lead to certain desirable behavior With contributions from more than 40 worldwide experts, this handbook equips readers with the necessary techniques and tools to solve problems in a variety of applications. Readers gain exposure to the theoretical and algorithmic foundations of a wide range of topics in graph theory and combinatorial optimization, enabling them to identify (and hence solve) problems encountered in diverse disciplines, such as electrical, communication, computer, social, transportation, biological, and other networks.
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English [en] · PDF · 7.5MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save
base score: 11065.0, final score: 45.241356
nexusstc/Graph algorithms and applications 4/b48393de00735e8519ccf5d8af795dbc.pdf
Graph algorithms and applications 4 Giuseppe Liotta; Roberto Tamassia; Ioannis G Tollis; ebrary, Inc World Scientific Publishing Company, Incorporated, World Scientific Publishing Company, Singapore, 2006
This book contains Volume 7 of the "Journal of Graph Algorithms and Applications" (JGAA). JGAA is a peer-reviewed scientific journal devoted to the publication of high-quality research papers on the analysis, design, implementation, and applications of graph algorithms. Areas of interest include computational biology, computational geometry, computer graphics, computer-aided design, computer and interconnection networks, constraint systems, databases, graph drawing, graph embedding and layout, knowledge representation, multimedia, software engineering, telecommunications networks, user interfaces and visualization, and VLSI circuit design. "Graph Algorithms and Applications 4" presents contributions from prominent authors and includes selected papers from the Seventh International Workshop on Algorithms and Data Structures (WADS 2001) and the 2001 Symposium on Graph Drawing (GD 2001). All papers in the book have extensive diagrams and offer a unique treatment of graph algorithms focusing on the important applications.
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English [en] · PDF · 11.4MB · 2006 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save
base score: 11065.0, final score: 45.077034
lgli/A:\usenetabtechnical\Journal of Graph Algorithms and Applications 4 (World, 2002) WW.pdf
Journal of Graph Algorithms and Applications 4 World, 2002
English [en] · PDF · 13.7MB · 2002 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/zlib · Save
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base score: 0.01, final score: 45.058147
nexusstc/Graph algorithms and applications 3/b88d7fc1cbf0249066eb20d8515257f3.pdf
Journal of Graph algorithms and applications vol. 6 no. 1 Giuseppe Liotta, Ph. D.; Roberto Tamassia; Ioannis G Tollis World Scientific Publishing Company, Journal of Graph Algorithms and Applications, 2004
This book contains Volume 6 of the Journal of Graph Algorithms and Applications (JGAA). JGAA is a peer-reviewed scientific journal devoted to the publication of high-quality research papers on the analysis, design, implementation, and applications of graph algorithms. Areas of interest include computational biology, computational geometry, computer graphics, computer-aided design, computer and interconnection networks, constraint systems, databases, graph drawing, graph embedding and layout, knowledge representation, multimedia, software engineering, telecommunications networks, user interfaces and visualization, and VLSI circuit design. Graph Algorithms and Applications 3 presents contributions from prominent authors and includes selected papers from the Symposium on Graph Drawing (1999 and 2000). All papers in the book have extensive diagrams and offer a unique treatment of graph algorithms focusing on the important applications.
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English [en] · PDF · 8.5MB · 2004 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save
base score: 11065.0, final score: 44.93445
nexusstc/Massive Graph Analytics/b80ffb7fc91de1675fccee5263a5bb3e.pdf
Massive Graph Analytics (Chapman & Hall/CRC Data Science Series) BARDERS,DAVID. A. CRC Press, Taylor & Francis Group, Chapman & Hall/CRC Data Science Series, First edition, Place of publication not identified, 2022
"Graphs. Such a simple idea. Map a problem onto a graph then solve it by searching over the graph or by exploring the structure of the graph. What could be easier? Turns out, however, that working with graphs is a vast and complex field. Keeping up is challenging. To help keep up, you just need an editor who knows most people working with graphs, and have that editor gather nearly 70 researchers to summarize their work with graphs. The result is the book Massive Graph Analytics." — Timothy G. Mattson, Senior Principal Engineer, Intel Corp Expertise in massive-scale graph analytics is key for solving real-world grand challenges from healthcare to sustainability to detecting insider threats, cyber defense, and more. This book provides a comprehensive introduction to massive graph analytics, featuring contributions from thought leaders across academia, industry, and government. Massive Graph Analytics will be beneficial to students, researchers, and practitioners in academia, national laboratories, and industry who wish to learn about the state-of-the-art algorithms, models, frameworks, and software in massive-scale graph analytics.
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English [en] · PDF · 25.1MB · 2022 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save
base score: 11065.0, final score: 44.901394
nexusstc/Graph algorithms and applications 2/002a52a2aa3309ec03c4e04b29734cfc.pdf
Graph algorithms and applications 2 Giuseppe Liotta, Roberto Tamassia, Ioannis G. Tollis World Scientific Publishing Company, World Scientific Publishing Company, River Edge, N.J., 2004
This book contains Volumes 4 and 5 of the Journal of Graph Algorithms and Applications (JGAA). The first book of this series, Graph Algorithms and Applications 1, published in March 2002, contains Volumes 1–3 of JGAA. JGAA is a peer-reviewed scientific journal devoted to the publication of high-quality research papers on the analysis, design, implementation, and applications of graph algorithms. Areas of interest include computational biology, computational geometry, computer graphics, computer-aided design, computer and interconnection networks, constraint systems, databases, graph drawing, graph embedding and layout, knowledge representation, multimedia, software engineering, telecommunications networks, user interfaces and visualization, and VLSI circuit design. The journal is supported by distinguished advisory and editorial boards, has high scientific standards, and takes advantage of current electronic document technology.Graph Algorithms and Applications 2 presents contributions from prominent authors and includes selected papers from the Dagstuhl Seminar on Graph Algorithms and Applications and the Symposium on Graph Drawing in 1998. All papers in the book have extensive diagrams and offer a unique treatment of graph algorithms focusing on the important applications.
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English [en] · PDF · 4.6MB · 2004 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save
base score: 11065.0, final score: 44.885193
lgli/N:\!genesis_files_for_add\_add\kolxo3\93\Cs_Computer science\CsAl_Algorithms\Gallier J., Quaintance J. Spectral theory of unsigned and signed graphs, applications to graph clustering (draft, 2019)(122s)_CsAl_.pdf
Spectral theory of unsigned and signed graphs, applications to graph clustering Gallier J., Quaintance J Graph, draft, 2019
English [en] · PDF · 1.1MB · 2019 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save
base score: 11057.0, final score: 44.75226
lgli/A:\usenetabtechnical\Journal of Graph Algorithms and Applications 2 (World, 2002) WW.pdf
Journal of Graph Algorithms and Applications, Volume 4 Brown University. Dept. of Computer Science Brown University Dept. of Computer Science], 2002
English [en] · PDF · 5.6MB · 2002 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/zlib · Save
❌ This file might have issues.
base score: 0.01, final score: 44.710846
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