《TF-GNN：Graph Neural Networks》附录《A.2.2 Creating GraphTensors》例程

微软concept graph数据，可以使用neo4j进行使用。原始网站concept.research.microsoft.com已经挂掉，幸好下载保存了一份数据，可以用neo4j进行调用，教程： https://neo4j.com/blog/microsoft-concept-graph-neo4j/

数据结构图投资组合分配 使用邻接矩阵或邻接表和各种算法（例如深度优先搜索，广度优先搜索和Dijkstra算法）存储的无向和有向图抽象数据类型的Python3实现。 该项目在2021年冬季季度完成，在Tim Alcon教授的指导下，在OSU的CS 261数据结构课程中完成。 这两个文件中已经提供了各种测试用例。 只需运行： $ ./d_graph.py 或者 $ ./ud_graph.py

PDG：程序依赖图 由以下规则定义的非常基本的命令性程序的程序依赖关系图创建器。 assignment := x=Aexpr; conditional1 := if(Bexpr){ stmts; } else {stmts; } endif conditional2 := if(Bexpr){ stmts; } endif iterative := while(Bexpr){stmts; } stmt := assignment | conditionsal1 | conditional2 | iterative stmts := stmt | stmts; stmt Aexpr: = constant | variable | Aexpr + Aexpr | Aexpr – Aexpr | Aexpr * Aexpr | Aexpr /

这是一个用C++写的graph cut代码，对分割图像有很好的效果。希望有帮助

Yuri Boykov, Vladimir Kolmogorov的代码

深度优先遍历，宽度优先遍历. 程序从图文本中读取图的矩阵。 矩阵包括有向图或无向图

一种新的基于特征选择的图同构算法在网络模体发现中的应用，呼加璐，孙玲，模体是在真实网络中出现频率明显高于随机网络中出现频率的子图。模体查找需要对出现的子图计数，因此引发了子图同构的问题。但是

unity图表插件包 Graph and chart 最新版

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