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# 统计代写|网络分析代写Network Analysis代考|COMP_SCI396 The Common Representation of Graphs

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## 统计代写|网络分析代写Network Analysis代考|Introduction

Undirected:
The edges do not have any directions.
Directed Networks:
The edges have directions.

b. Simple:
The graph has only one link type.

Multigraph:
The graph can have more than one same link type.
c. Unweighted:
The edges in the graph do not contain weight.
Weighted:
The edge in the graph contains value (numerical), which is known as weight.

Other important graphs:
i. Regular graph,
ii. Complete graph,
iii. Path,
iv. Cycle,
v. Bipartitie graph,
vi. Euler graph,
vii. Hamilton graph,
viii. Planner,
ix. Tree and forest, and so on.

## 统计代写|网络分析代写Network Analysis代考|Important Terms to Remember in Graph Representation

a. Centrality measures
Centrality measures are a significant indicator used in network analysis. There are different types of centrality measures. Some prominent measures are given as follows:

• Betweenness centrality
Assuming the important nodes connect other nodes. The betweenness centrality is defined as the cumulative sum of ratios of the paths between two nodes through a node to the total number of shortest paths available between those nodes.
• Closeness centrality
Assuming in a connected graph, closeness centrality is a measure of centrality in the given network. The node is closer to all nodes if it is more central.Degree centrality
• Assuming the networks where all nodes are connected and one or more than one nodes have predominant connections in comparison with other neighbouring nodes For instance, in an undirected graph, the degree centrality is defined by the number of connections attached to each node.
• Eigenvector centrality Assuming the networks where all nodes are connected and one or more than one nodes have predominant connections in comparison with other neighboring nodes. Eigenvector centrality is an algorithm that measures the influence or connectivity of nodes [2]. Relationships to high-scoring nodes contribute more to the score of a node than connections to low-scoring nodes. A high score means that a node is connected to other nodes that have high scores.
• PageRank centrality
• Assuming the networks where all nodes are connected and one or more than one node have predominant connections in comparison with other neighboring nodes. For instance, nodes relate to links representing appropriate weights and weights are updated when the node centrality/significance changes in the directed network [3].
• b. Geodesic distance
• c. Networks
• Distributed
• Centralized
• Decentralized

C。未加权：

i。正则图，
ii。完整的图表，
iii。路径，

## 统计代写|网络分析代写NETWORK ANALYSIS代考|IMPORTANT TERMS TO REMEMBER IN GRAPH REPRESENTATION

• 中介中心性
假设重要节点连接其他节点。中介中心性定义为两个节点之间通过一个节点的路径与这些节点之间可用的最短路径总数之比的累积和。
• 接近中心性
假设在一个连通图中，接近中心性是给定网络中中心性的度量。如果节点更中心，则该节点更接近所有节点。度中心性
• 假设所有节点都连接在一起并且一个或多个节点与其他相邻节点相比具有主要连接的网络例如，在无向图中，度中心性由连接到每个节点的连接数定义。
• 特征向量中心性 假设所有节点都已连接并且一个或多个节点与其他相邻节点相比具有主要连接的网络。特征向量中心性是一种衡量节点影响或连通性的算法[2]。与得分高的节点的关系比与得分低的节点的连接对节点得分的贡献更大。高分意味着一个节点连接到具有高分的其他节点。
• PageRank 中心性
• 假设所有节点都已连接并且一个或多个节点与其他相邻节点相比具有主要连接的网络。例如，节点与代表适当权重的链接相关，当有向网络中的节点中心性/重要性发生变化时，权重会更新[3]。
• 湾。测地距离
• C。网络
• 分散式
• 集中
• 去中心化

## Matlab代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。