### 计算机代写|神经网络代写neural networks代考|STAT3007

statistics-lab™ 为您的留学生涯保驾护航 在代写神经网络neural networks方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写神经网络neural networks代写方面经验极为丰富，各种代写神经网络neural networks相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 计算机代写|神经网络代写neural networks代考|Representation Learning for Networks

Beyond popular data like images, texts, and sounds, network data is another important data type that is becoming ubiquitous across a large scale of real-world applications ranging from cyber-networks (e.g., social networks, citation networks, telecommunication networks, etc.) to physical networks (e.g., transportation networks, biological networks, etc). Networks data can be formulated as graphs mathematically, where vertices and their relationships jointly characterize the network information. Networks and graphs are very powerful and flexible data formulation such that sometimes we could even consider other data types like images, and texts as special cases of it. For example, images can be considered as grids of nodes with RGB attributes which are special types of graphs, while texts can also be organized into sequential-, tree-, or graph-structured information. So in general, representation learning for networks is widely considered as a promising yet more challenging tasks that require the advancement and generalization of many techniques we developed for images, texts, and so forth. In addition to the intrinsic high complexity of network data, the efficiency of representation learning on networks is also an important issues considering the large-scale of many real-world networks, ranging from hundreds to millions or even billions of vertices. Analyzing information networks plays a crucial role in a variety of emerging applications across many disciplines. For example, in social networks, classifying users into meaningful social groups is useful for many important tasks, such as user search, targeted advertising and recommendations; in communication networks, detecting community structures can help better understand the rumor spreading process; in biological networks, inferring interactions between proteins can facilitate new treatments for diseases. Nevertheless, efficient and effective analysis of these networks heavily relies on good representations of the networks.

## 计算机代写|神经网络代写neural networks代考|Graph Representation Learning: An Introduction

Many complex systems take the form of graphs, such as social networks, biological networks, and information networks. It is well recognized that graph data is often sophisticated and thus is challenging to deal with. To process graph data effectively, the first critical challenge is to find effective graph data representation, that is, how to represent graphs concisely so that advanced analytic tasks, such as pattern discovery, analysis, and prediction, can be conducted efficiently in both time and space.

Traditionally, we usually represent a graph as $\mathscr{G}=(\mathscr{V}, \mathscr{E})$, where $\mathscr{V}$ is a node set and $\mathscr{E}$ is an edge set. For large graphs, such as those with billions of nodes, the traditional graph representation poses several challenges to graph processing and analysis.
(1) High computational complexity. These relationships encoded by the edge set $E$ take most of the graph processing or analysis algorithms either iterative or combinatorial computation steps. For example, a popular way is to use the shortest or average path length between two nodes to represent their distance. To compute such a distance using the traditional graph representation, we have to enumerate many possible paths between two nodes, which is in nature a combinatorial problem. Such methods result in high computational complexity that prevents them from being applicable to large-scale real-world graphs.
(2) Low parallelizability. Parallel and distributed computing is de facto to process and analyze large-scale data. Graph data represented in the traditional way, however, casts severe difficulties to design and implementat of parallel and distributed algorithms. The bottleneck is that nodes in a graph are coupled to each other explicitly reflected by $E$. Thus, distributing different nodes in different shards or servers often causes demandingly high communication cost among servers, and holds back speed-up ratio.

## 计算机代写|神经网络代写neural networks代考|Graph Representation Learning: An Introduction

(1) 计算复杂度高。这些由边集编码的关系和采用大多数图形处理或分析算法迭代或组合计算步骤。例如，一种流行的方法是使用两个节点之间的最短或平均路径长度来表示它们的距离。为了使用传统的图形表示来计算这样的距离，我们必须枚举两个节点之间的许多可能路径，这本质上是一个组合问题。这样的方法导致高计算复杂性，从而阻止它们适用于大规模的真实世界图。
(2) 并行性低。并行和分布式计算实际上是处理和分析大规模数据。然而，以传统方式表示的图形数据给并行和分布式算法的设计和实现带来了严重的困难。瓶颈是图中的节点相互耦合，显式反映为和. 因此，将不同的节点分布在不同的分片或服务器中往往会导致服务器之间的通信成本很高，并且会阻碍加速比。

## 有限元方法代写

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## 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。