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数据科学代写|复杂网络代写Complex Network代考|Related Works

如果你也在 怎样代写复杂网络Complex Network 这个学科遇到相关的难题,请随时右上角联系我们的24/7代写客服。复杂网络Complex Network在网络理论的背景下,复杂网络是指具有非微观拓扑特征的图(网络)–这些特征在简单的网络(如格子或随机图)中不会出现,但在代表真实系统的网络中经常出现。复杂网络的研究是一个年轻而活跃的科学研究领域(自2000年以来),主要受到现实世界网络的经验发现的启发,如计算机网络、生物网络、技术网络、大脑网络、气候网络和社会网络。

复杂网络Complex Network大多数社会、生物和技术网络显示出实质性的非微观拓扑特征,其元素之间的连接模式既不是纯粹的规则也不是纯粹的随机。这些特征包括学位分布的重尾、高聚类系数、顶点之间的同态性或异态性、社区结构和层次结构。在有向网络的情况下,这些特征还包括互惠性、三联体重要性概况和其他特征。相比之下,过去研究的许多网络的数学模型,如格子和随机图,并没有显示这些特征。最复杂的结构可以由具有中等数量相互作用的网络实现。这与中等概率获得最大信息含量(熵)的事实相对应。

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数据科学代写|复杂网络代写Complex Network代考|Related Works

数据科学代写|复杂网络代写Complex Network代考|Related Works

Collaboration networks, where vertices represent people and two people are connected if they have ever collaborated, are among the earliest and most studied networks in network science $[4,11,13,23,24,31]$. Many of these collaboration networks include only vertices from a certain field, for example, where vertices represent mathematicians and two mathematicians are connected if they have coauthored a paper [18]. Less work has been done to study collaboration networks with an eye toward geospatial organization. However, Abramo et al. found that among Italian academics women researchers register a greater capacity to collaborate, with the exception of international collaboration, where there is still a gap in comparison to male colleagues [2]. Pan et el. found that collaboration strengths between cities decrease with an increase in distance and follow gravity laws [25]. Fortunato et al. found that scientist who left their country of origin had higher citation scores than those who did not [15].

There has been much work studying collaboration networks with respect to gender, but much less with respect to race and ethnicity. Ductor et el. showed that women have fewer collaborators, collaborate more often with the same coauthors, and a higher fraction of their co-authors are co-authors of each other (they have a higher clustering coefficient) [14]. Holman and Morandin examined similar properties in the realm of life science by studying the PubMed biomedical article database and found that researchers preferentially co-publish with colleagues of the same gender, and show that this ‘gender homophily’ is slightly stronger today than it was 10 years ago [20]. Using a dataset with more than 270,000 scientists from Brazil, Araújo et al. show that while men are more likely to collaborate with other men, women are more egalitarian regardless of how many collaborators each scientist has, with the exception of engineering where the bias disappears with increasing number of collaborators [3]. West et al. showed that on average women publish fewer papers than men [32]. These related works use various tools such as the gender-api.com, or US Social Security Administration website to help infer gender of researchers.

数据科学代写|复杂网络代写Complex Network代考|System Overview and Methods of Construction

We present information here about the construction, visualization and analysis of the academic collaboration network using a variety of computational tools and applications built by the research team.

The current network includes: 3,751 seed vertices from the Cal Poly Collaboration Network with 12,924 collaborator vertices and 27,103 seed vertices from the UC Computing Collaboration Network with 463,854 collaborator vertices. This data was gathered from Scopus, Elsevier’s abstract and citation database [6]. Tables containing the researchers affiliated with Cal Poly and the ten universities in the University of California system were downloaded from the website. Scopus provides each author with a set of subject areas. For Cal Poly, all researchers were included. For the UCs, only researchers with a subject of computer science were considered. This is a much broader category than the faculty members of the UC computer science departments, and includes all researchers working in a field related to computer science. For example, approximately 10-20\% of researchers from each UC school had a subject area including ‘computer science’, which included many researchers whose primary department was from other fields. This wider net allows us to examine the computing field as a whole.

In the Scopus system, each author is given a unique ID. The author name, surname, and list of publications were collected through the use of the Scopus API using this ID. Data access was managed via the open source python library pybliometrics [28]. A unique identifier for each publication was obtained, which was used for a second data query for publication data. Specifically, information was gathered about publication title, venue, citation count, list of coauthors, and the affiliation ID for each coauthor. For some publications, both the department and university of the researchers were provided. Other times, only the university was provided. Both were collected when available. Finally, the affiliation IDs were queried to yield the affiliation name, city, state, country, and postal code. Latitude and longitude coordinates were obtained from these fields using the open source library GeoPy. Location was derived with the most specific data possible (i.e. affiliation name, city, state, and country). Most locations were able to be identified from either the affiliation name or city. Future work will address visualizing any geospatial errors.


数据科学代写|复杂网络代写Complex Network代考|Related Works

复杂网络代写

数据科学代写|复杂网络代写Complex Network代考|Related Works

Collaboration networks, where vertices represent people and two people are connected if they have ever collaborated, are among the earliest and most studied networks in network science $[4,11,13,23,24,31]$. Many of these collaboration networks include only vertices from a certain field, for example, where vertices represent mathematicians and two mathematicians are connected if they have coauthored a paper [18]. Less work has been done to study collaboration networks with an eye toward geospatial organization. However, Abramo et al. found that among Italian academics women researchers register a greater capacity to collaborate, with the exception of international collaboration, where there is still a gap in comparison to male colleagues [2]. Pan et el. found that collaboration strengths between cities decrease with an increase in distance and follow gravity laws [25]. Fortunato et al. found that scientist who left their country of origin had higher citation scores than those who did not [15].

There has been much work studying collaboration networks with respect to gender, but much less with respect to race and ethnicity. Ductor et el. showed that women have fewer collaborators, collaborate more often with the same coauthors, and a higher fraction of their co-authors are co-authors of each other (they have a higher clustering coefficient) [14]. Holman and Morandin examined similar properties in the realm of life science by studying the PubMed biomedical article database and found that researchers preferentially co-publish with colleagues of the same gender, and show that this ‘gender homophily’ is slightly stronger today than it was 10 years ago [20]. Using a dataset with more than 270,000 scientists from Brazil, Araújo et al. show that while men are more likely to collaborate with other men, women are more egalitarian regardless of how many collaborators each scientist has, with the exception of engineering where the bias disappears with increasing number of collaborators [3]. West et al. showed that on average women publish fewer papers than men [32]. These related works use various tools such as the gender-api.com, or US Social Security Administration website to help infer gender of researchers.

数据科学代写|复杂网络代写Complex Network代考|System Overview and Methods of Construction

We present information here about the construction, visualization and analysis of the academic collaboration network using a variety of computational tools and applications built by the research team.

The current network includes: 3,751 seed vertices from the Cal Poly Collaboration Network with 12,924 collaborator vertices and 27,103 seed vertices from the UC Computing Collaboration Network with 463,854 collaborator vertices. This data was gathered from Scopus, Elsevier’s abstract and citation database [6]. Tables containing the researchers affiliated with Cal Poly and the ten universities in the University of California system were downloaded from the website. Scopus provides each author with a set of subject areas. For Cal Poly, all researchers were included. For the UCs, only researchers with a subject of computer science were considered. This is a much broader category than the faculty members of the UC computer science departments, and includes all researchers working in a field related to computer science. For example, approximately 10-20\% of researchers from each UC school had a subject area including ‘computer science’, which included many researchers whose primary department was from other fields. This wider net allows us to examine the computing field as a whole.

In the Scopus system, each author is given a unique ID. The author name, surname, and list of publications were collected through the use of the Scopus API using this ID. Data access was managed via the open source python library pybliometrics [28]. A unique identifier for each publication was obtained, which was used for a second data query for publication data. Specifically, information was gathered about publication title, venue, citation count, list of coauthors, and the affiliation ID for each coauthor. For some publications, both the department and university of the researchers were provided. Other times, only the university was provided. Both were collected when available. Finally, the affiliation IDs were queried to yield the affiliation name, city, state, country, and postal code. Latitude and longitude coordinates were obtained from these fields using the open source library GeoPy. Location was derived with the most specific data possible (i.e. affiliation name, city, state, and country). Most locations were able to be identified from either the affiliation name or city. Future work will address visualizing any geospatial errors.

数据科学代写|复杂网络代写Complex Network代考

数据科学代写|复杂网络代写Complex Network代考 请认准UprivateTA™. UprivateTA™为您的留学生涯保驾护航。

微观经济学代写

微观经济学是主流经济学的一个分支,研究个人和企业在做出有关稀缺资源分配的决策时的行为以及这些个人和企业之间的相互作用。my-assignmentexpert™ 为您的留学生涯保驾护航 在数学Mathematics作业代写方面已经树立了自己的口碑, 保证靠谱, 高质且原创的数学Mathematics代写服务。我们的专家在图论代写Graph Theory代写方面经验极为丰富,各种图论代写Graph Theory相关的作业也就用不着 说。

线性代数代写

线性代数是数学的一个分支,涉及线性方程,如:线性图,如:以及它们在向量空间和通过矩阵的表示。线性代数是几乎所有数学领域的核心。

博弈论代写

现代博弈论始于约翰-冯-诺伊曼(John von Neumann)提出的两人零和博弈中的混合策略均衡的观点及其证明。冯-诺依曼的原始证明使用了关于连续映射到紧凑凸集的布劳威尔定点定理,这成为博弈论和数学经济学的标准方法。在他的论文之后,1944年,他与奥斯卡-莫根斯特恩(Oskar Morgenstern)共同撰写了《游戏和经济行为理论》一书,该书考虑了几个参与者的合作游戏。这本书的第二版提供了预期效用的公理理论,使数理统计学家和经济学家能够处理不确定性下的决策。

微积分代写

微积分,最初被称为无穷小微积分或 “无穷小的微积分”,是对连续变化的数学研究,就像几何学是对形状的研究,而代数是对算术运算的概括研究一样。

它有两个主要分支,微分和积分;微分涉及瞬时变化率和曲线的斜率,而积分涉及数量的累积,以及曲线下或曲线之间的面积。这两个分支通过微积分的基本定理相互联系,它们利用了无限序列和无限级数收敛到一个明确定义的极限的基本概念 。

计量经济学代写

什么是计量经济学?
计量经济学是统计学和数学模型的定量应用,使用数据来发展理论或测试经济学中的现有假设,并根据历史数据预测未来趋势。它对现实世界的数据进行统计试验,然后将结果与被测试的理论进行比较和对比。

根据你是对测试现有理论感兴趣,还是对利用现有数据在这些观察的基础上提出新的假设感兴趣,计量经济学可以细分为两大类:理论和应用。那些经常从事这种实践的人通常被称为计量经济学家。

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。

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