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Edmonds开花算法，用于无向图中的最大权重匹配 该库实现了Blossom算法，该算法计算O（节点数** 3）中无向图的最大加权匹配。 它从Joris van Rantwijk编写的python代码移植而来，该代码包含在NetworkX图形库中并进行了修改。 入门 将必要的依赖项添加到您的项目中： [ageneau/blossom " 0.1.4 " ] [aysylu/loom " 1.0.2 " ] 用法 ( ns test.blossom ( :require [blossom.max-weight-matching :as mwm] [blossom.matching :as m] [loom.graph :as lg])) ( def edges [[ 1 2 2 ][ 1 3 -2 ][ 2 3 1 ][ 2 4 -1 ][ 3 4 -6 ]]) ( def g ( -> ( lg/weighted-graph ) ( lg/add-edges* edges))) ; ; Compute a maximum weig

Shimon Even's Graph Algorithms, published in 1979, was a seminal introductory book on algorithms read by everyone engaged in the field. This thoroughly revised second edition, with a foreword by Richard M. Karp and notes by Andrew V. Goldberg, continues the exceptional presentation from the first edition and explains algorithms in a formal but simple language with a direct and intuitive presentation. The book begins by covering basic material, including graphs and shortest paths, trees, depth-first-search and breadth-first search. The main part of the book is devoted to network flows and applications of network flows, and it ends with chapters on planar graphs and testing graph planarity.

Graph Algorithms by Mark Needham and Amy E. Hodler Copyright © 2019 Amy Hodler and Mark Needham. All rights reserved. What’s in This Book This book is a practical guide to getting started with graph algorithms for developers and data scientists who have experience using Apache Spark™ or Neo4j. Although our algorithm examples utilize the Spark and Neo4j platforms, this book will also be helpful for understanding more general graph concepts, regardless of your choice of graph technologies. The first two chapters provide an introduction to graph analytics, algorithms, and theory. The third chapter briefly covers the platforms used in this book before we dive into three chapters focusing on classic graph algorithms: pathfinding, centrality, and community detection. We wrap up the book with two chapters showing how graph algorithms are used within workflows: one for general analysis and one for machine learning. At the beginning of each category of algorithms, there is a reference table to help you quickly jump to the relevant algorithm. For each algorithm, you’ll find: • An explanation of what the algorithm does • Use cases for the algorithm and references to where you can learn more • Example code providing concrete ways to use the algorithm in Spark, Neo4j, or both 图方法方面最新的参考书，本文理论实践兼备（看标题就知道了），内容高清无码书签完整诚不我欺，强烈推荐给需要的朋友！

Algorithms.in.C++.Part.5.Graph.Algorithms