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assignmentutor-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
• (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|Graph Neural Networks: Organization

The high-level organization of the book is demonstrated in Figure 1.3. The book is organized into four parts to best accommodate a variety of readers. Part I introduces basic concepts; Part II discusses the most established methods; Part III presents the most typical frontiers, and Part IV describes advances of methods and applications that tend to be important and promising for future research. Next, we briefly elaborate on each chapter.

• Part I: Introduction. These chapters provide the general introduction from the representation learning for different data types, to the graph representation learning. In addition, it introduces the basic ideas and typical variants of graph neural networks for the graph representation learning.
• Part II: Foundations. These chapters describe the foundations of the graph neural networks by introducing the properties of graph neural networks as well as several fundamental problems in this line. Specifically, this part introduces the fundamental problems in graphs: node classification, the expressive power of graph neural networks, the interpretability and scalability issues of graph neural network, and the adversarial robustness of the graph neural networks.
• Part III: Frontiers. In these chapters, some frontier or advanced problems in the domain of graph neural networks are proposed. Specifically, there are introductions about the techniques in graph classification, link prediction, graph generation, graph transformation, graph matching, graph structure learning. In addition, there are also introductions of several variants of GNNs for different types of graphs, such as GNNs for dynamic graphs, heterogeneous graphs. We also introduce the AutoML and self-supervised learning for GNNs.
• Part IV: Broad and Emerging Applications. These chapters introduce the broad and emerging applications with GNNs. Specifically, these GNNs-based applications covers modern recommender systems, tasks in computer vision and NLP, program analysis, software mining, biomedical knowledge graph mining for drug design, protein function prediction and interaction, anomaly detection, and urban intelligence.

## 计算机代写|神经网络代写neural networks代考|Background and Problem Defnition

Graph-structured data (e.g., social networks, the World Wide Web, and proteinprotein interaction networks) are ubiquitous in real-world, covering a variety of applications. A fundamental task on graphs is node classification, which tries to classify the nodes into a few predefined categories. For example, in social networks, we want to predict the political bias of each user; in protein-protein interaction networks, we are interested in predicting the function role of each protein; in the World Wide Web, we may have to classify web pages into different semantic categories. To make effective prediction, a critical problem is to have very effective node representations, which largely determine the performance of node classification.
Graph neural networks are neural network architectures specifically designed for learning representations of graph-structured data including learning node represen-tations of big graphs (e.g., social networks and the World Wide Web) and learning representations of entire graphs (e.g., molecular graphs). In this chapter, we will focus on learning node representations for large-scale graphs and will introduce learning the whole-graph representations in other chapters. A variety of graph neural networks have been proposed (Kipf and Welling, 2017b; Velicković et al, 2018; Gilmer et al, 2017; Xhonneux et al, 2020; Liao et al, 2019b; Kipf and Welling, 2016; Veličković et al, 2019). In this chapter, we will comprehensively revisit existing graph neural networks for node classification including supervised approaches (Sec. 4.2), unsupervised approaches (Sec. 4.3), and a common problem of graph neural networks for node classification-over-smoothing (Sec. 4.4).

Problem Definition. Let us first formally define the problem of learning node representations for node classification with graph neural networks. Let $\mathscr{G}=(\mathscr{V}, \mathscr{E})$ denotes a graph, where $\mathscr{V}$ is the set of nodes and $\mathscr{E}$ is the set of edges. $A \in R^{N \times N}$ represents the adjacency matrix, where $N$ is the total number of nodes, and $X \in R^{N \times C}$ represents the node attribute matrix, where $C$ is the number of features for each node. The goal of graph neural networks is to learn effective node representations (denoted as $H \in R^{N \times F}, F$ is the dimension of node representations) by combining the graph structure information and the node attributes, which are further used for node classification.

# 深度学习代写

## 计算机代写|神经网络代写neural networks代考|Graph Neural Networks: Organization

• 第一部分：简介。这些章节提供了从不同数据类型的表示学习到图表示学习的一般介绍。此外，还介绍了用于图表示学习的图神经网络的基本思想和典型变体。
• 第二部分：基础。这些章节通过介绍图神经网络的性质以及这方面的几个基本问​​题来描述图神经网络的基础。具体来说，这部分介绍了图的基本问题：节点分类、图神经网络的表达能力、图神经网络的可解释性和可扩展性问题，以及图神经网络的对抗鲁棒性。
• 第三部分：前沿。在这些章节中，提出了图神经网络领域的一些前沿或高级问题。具体介绍了图分类、链接预测、图生成、图变换、图匹配、图结构学习等技术。此外，还介绍了针对不同类型图的 GNN 的几种变体，例如针对动态图、异构图的 GNN。我们还介绍了 GNN 的 AutoML 和自我监督学习。
• 第四部分：广泛的新兴应用。这些章节介绍了 GNN 的广泛和新兴应用。具体来说，这些基于 GNN 的应用涵盖了现代推荐系统、计算机视觉和 NLP 中的任务、程序分析、软件挖掘、用于药物设计的生物医学知识图谱挖掘、蛋白质功能预测和交互、异常检测和城市智能。

## 有限元方法代写

assignmentutor™作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

assignmentutor™您的专属作业导师
assignmentutor™您的专属作业导师