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.
Table of Contents
SECTION I - Basic Concepts and Algorithms
CHAPTER 1 - Basic Concepts in Graph Theory and Algorithms
CHAPTER 2 - Basic Graph Algorithms
CHAPTER 3 - Depth-First Search and Applications
SECTION II - Flows in Networks
CHAPTER 4 - Maximum Flow Problem
CHAPTER 5 - Minimum Cost Flow Problem
CHAPTER 6 - Multicommodity Flows
SECTION III - Algebraic Graph Theory
CHAPTER 7 - Graphs and Vector Spaces
CHAPTER 8 - Incidence, Cut, and Circuit Matrices of a Graph
CHAPTER 9 - Adjacency Matrix and Signal Flow Graphs
CHAPTER 10 - Adjacency Spectrum and the Laplacian Spectrum of a Graph
CHAPTER 11 - Resistance Networks, Random Walks, and Network Theorems
SECTION IV - Structural Graph Theory
CHAPTER 12 - Connectivity
CHAPTER 13 - Connectivity Algorithms
CHAPTER 14 - Graph Connectivity Augmentation
CHAPTER 15 - Matchings
CHAPTER 16 - Matching Algorithms
CHAPTER 17 - Stable Marriage Problem
CHAPTER 18 - Domination in Graphs
CHAPTER 19 - Graph Colorings
SECTION V - Planar Graphs
CHAPTER 20 - Planarity and Duality
CHAPTER 21 - Edge Addition Planarity Testing Algorithm
CHAPTER 22 - Planarity Testing Based on PC-Trees
CHAPTER 23 - Graph Drawing
SECTION VI - Interconnection Networks
CHAPTER 24 - Introduction to Interconnection Networks
CHAPTER 25 - Cayley Graphs
CHAPTER 26 - Graph Embedding and Interconnection Networks
SECTION VII - Special Graphs
CHAPTER 27 - Program Graphs
CHAPTER 28 - Perfect Graphs
CHAPTER 29 - Tree-Structured Graphs
SECTION VIII - Partitioning
CHAPTER 30 - Graph and Hypergraph Partitioning
SECTION IX - Matroids
CHAPTER 31 - Matroids
CHAPTER 32 - Hybrid Analysis and Combinatorial Optimization
SECTION X - Probabilistic Methods, Random Graph Models, and Randomized Algorithms
CHAPTER 33 - Probabilistic Arguments in Combinatorics
CHAPTER 34 - Random Models and Analyses for Chemical Graphs
CHAPTER 35 - Randomized Graph Algorithms: Techniques and Analysis
SECTION XI - Coping with NP-Completeness
CHAPTER 36 - General Techniques for Combinatorial Approximation
CHAPTER 37 - ε-Approximation Schemes for the Constrained Shortest Path Problem
CHAPTER 38 - Constrained Shortest Path Problem: Lagrangian Relaxation-Based Algorithmic Approaches
CHAPTER 39 - Algorithms for Finding Disjoint Paths with QoS Constraints
CHAPTER 40 - Set-Cover Approximation
CHAPTER 41 - Approximation Schemes for Fractional Multicommodity Flow Problems
CHAPTER 42 - Approximation Algorithms for Connectivity Problems
CHAPTER 43 - Rectilinear Steiner Minimum Trees
CHAPTER 44 - Parameter Algorithms and Complexity
2022-09-22 08:37:18
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Kinect VFX图形
这是一个如何将Kinect中的深度相机提要与Unity的VFX图形一起使用的示例。 该项目中的代码在很大程度上受到高桥敬二郎（ Takahashi）的的启发。
要求
支持VFX图形的Unity版本
Unity软件包依赖于Kinect for Windows Unity软件包，可在单独下载。
如何使用
将Kinect VFX预制件添加到场景中。 在您的VFX图形中，使用KinectPointCloudMap渲染纹理作为“从地图设置位置”节点的输入。 KinectColorMap渲染纹理中提供了每个深度点的颜色数据。
另外，您也可以使用Kinect VFX映射的预制
2022-09-20 18:47:36
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纸
论文“深度融合集群网络”的源代码
图W.涂，周S.，刘X.，郭X，蔡Z.
被AAAI2021接受。
安装
克隆此仓库。
git clone https://github.com/WxTu/DFCN.git
Windows 10或Linux 18.04
的Python 3.7.5
脾气暴躁的1.18.0
斯克莱恩0.21.3
火炬视觉0.3.0
Matplotlib 3.2.1
准备
我们总共采用了六个数据集，包括三个图形数据集（ACM，DBLP和CITE）和三个非图形数据集（USPS，HHAR和REUT）。 要在这些数据集上训练模型，请从（访问代码：4622）或下载它们。
代码结构与用法
在这里，我们提供了PyTorch中的深度融合集群网络（DFCN）的实现，以及DBLP数据集上的执行示例（由于文件大小的限制）。 该存储库的组织方式如下：
load_data.py
2022-09-15 10:00:13
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in-painting, graph-cut, bilateral filtering on images，有注释，测试用例，函数
2022-09-14 22:01:12
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EGNN-Pytorch（WIP）
中的实现。 最终可用于Alphafold2复制。
安装
$ pip install egnn-pytorch
用法
import torch
from egnn_pytorch import EGNN
layer1 = EGNN ( dim = 512 )
layer2 = EGNN ( dim = 512 )
feats = torch . randn ( 1 , 16 , 512 )
coors = torch . randn ( 1 , 16 , 3 )
feats , coors = layer1 ( feats , coors )
feats , coors = layer2 ( feats , coors ) # (1, 16, 512), (1, 16, 3)
带边
import torch
from egnn_pytorch impo
2022-09-13 00:58:52
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MMS骨架
介绍
MMSkeleton是一个开源工具箱，用于基于骨骼的人类理解。 这是负责的项目的一部分。 MMSkeleton是根据我们的研究项目。
更新
[2020-01-21] MMSkeleton v0.7发布。
[2019-10-09] MMSkeleton v0.6发布。
[2019-10-08]支持示范动物园。
[2019-10-02]支持自定义数据集。
[2019-09-23]添加基于视频的姿势估计演示。
[2019-08-29] MMSkeleton v0.5发布。
产品特点
高扩展性
MMSkeleton提供了灵活的框架来系统地组织代码和项目，并具有扩展到各种任务
2022-09-05 10:40:39
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纯PB源码的graph曲线，比自带的曲线漂亮、灵活，易于开发、集成，需要的可以下载测试使用，有问题请及时反馈。。。
2022-09-01 14:05:39
27KB
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nebula-graph-3.2.0.el7.x86_64 安装包，包含客户端console
2022-08-23 20:00:38
75.42MB
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