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Social Network Analysis

Social Network Analysis

本课程是多个项目的一部分。

位教师：Ian McCulloh

2,770 人已注册

包含在中

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4个模块

深入了解一个主题并学习基础知识。

11 条评论

中级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

11 条评论

中级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Learn to calculate and interpret key centrality measures to identify influential nodes in social networks.
Gain skills in applying statistical models to analyze relationships and dynamics within social networks.
Understand how foundational social theories inform network analysis and shape interpretations of social interactions.

您将获得的技能

您将学习的工具

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

9 项作业

授课语言：英语（English）

91%

of learners achieved a positive career outcome

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Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

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此课程作为的一部分提供

在注册此课程时，您还需要选择一个特定的合作项目。

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该课程共有4个模块

The "Social Network Analysis" course offers a comprehensive exploration of the intricate relationships within social networks, emphasizing the theoretical and practical applications of network analysis. Through engaging modules, learners will delve into advanced topics in graph theory, centrality measures, and statistical modeling, equipping them with the skills to analyze and interpret social structures effectively.

By completing this course, learners will gain a solid understanding of how to identify key influencers, measure network cohesion, and conduct hypothesis testing using empirical data. What sets this course apart is its blend of theoretical foundations and hands-on experience using R programming for network analysis, specifically with tools like 'statnet' and 'RSiena.' Whether you’re looking to enhance your skills in data analysis or seeking to understand the dynamics of social behavior, this course will serve as a vital resource. With a focus on real-world applications, learners will emerge equipped to tackle complex social phenomena, making significant contributions to their fields.

单元详情

This course explores the intersection of social theories and statistical analysis within social networks, focusing on structural dependence and its implications. You will engage in hypothesis testing of social forces using empirical data, and will learn to construct networks and model longitudinal behavior with tools such as 'statnet' and 'RSiena.' Key terminology and the hierarchy of social link formation will be emphasized, alongside practical calculations of fundamental graph and network measures like Density and Degree. Additionally, you will be able to differentiate between various network types and centrality measures, equipping them with a comprehensive understanding of social network analysis.

涵盖的内容

1篇阅读材料1个插件

In this module, you will explore advanced topics in graph theory and centrality measures as applied to social networks. You will learn to identify key influencers, measure network cohesion, and strategize interventions based on network structure and dynamics.

涵盖的内容

6个视频1篇阅读材料3个作业1个非评分实验室

6个视频总计77分钟

Terminology24分钟
Degree Centrality17分钟
Betweenness Centrality14分钟
Closeness Centrality7分钟
Centrality PE8分钟
Graph Level Measures7分钟

1篇阅读材料总计10分钟

Reading References10分钟

3个作业总计90分钟

Graph Theory and Centrality Measures60分钟
Introduction to Graph Theory and Network Types18分钟
Centrality Measures in Social Networks12分钟

1个非评分实验室总计60分钟

Practice Lab: Graph Theory & Centrality Measures60分钟

In this module, you will explore Graph Theory and Centrality Measures, delving into the dynamics of social networks. You will also learn to distinguish between the six social forces and understand the hierarchical formation of social links. You will discuss foundational social theories that underpin social network analysis, providing insights into how these theories shape organizational networks and societal interactions. This module equips you with essential knowledge to analyze and interpret the intricate relationships within social structures.

涵盖的内容

4个视频2篇阅读材料3个作业1个非评分实验室

4个视频总计79分钟

Social Theory Part 1 27分钟
Social Theory Part 230分钟
Social Theory Part 310分钟
Organizational Theories12分钟

2篇阅读材料总计55分钟

Reading References15分钟
Self-Reflective Reading: Looking Glass Self40分钟

3个作业总计90分钟

Centralization and Social Theory60分钟
Understanding Social Forces and Link Formation12分钟
Social Theories and Organizational Networks18分钟

1个非评分实验室总计60分钟

Practice Lab: Social Network Analysis Using R60分钟

In this module, you will explore Network Statistical Methods through a comprehensive study of structural dependence and its impact on statistical analysis. You will also learn to calculate link likelihoods manually and conduct hypothesis testing on social forces using empirical data. You will also gain practical skills in constructing Exponential Random Graph Models (ERGM) using ‘statnet’ in R and modeling longitudinal network behavior with Stochastic Actor Oriented Models (SAOM) using ‘RSiena’.