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所在平台: Udemy |
课程主页: https://www.udemy.com/course/social-network-analysis/
课程评论：没有评论
**课程总结：** 本课程《社交网络分析》深入探讨了如何理解和分析现实世界中人、信息、事件和地点之间错综复杂的连接，尤其是在线社交媒体的兴起使得这一分析变得尤为重要。 课程从**图论**的基础概念讲起，逐步扩展到**复杂网络**的理论，并重点关注这些分析技术在解决现实问题中的应用。 在实践层面，课程将使用 **Python** 语言及其相关库进行演示，包括： * **NetworkX**：用于构建和操作网络。 * **Matplotlib**：用于绘制和可视化网络。 * **NumPy** 和 **Pandas**：用于数据的读取和呈现。 此外，课程还将介绍 **Gephi** 这一强大的可视化和分析工具，帮助你通过其用户界面进行各种数据分析。 你将学习如何**准备数据**，并将这些关系映射化，从而理解人们的沟通和信息交换方式。课程将阐述**链接分析**的多种技术，用于评估节点的重要性，例如： * **中心性（Centrality）** * **声望（Prestige）** * **PageRank 算法** 课程还将特别强调**图遍历（Graph Traversals）**的理解，特别是**最短路径算法**，以及如何利用**图着色（Graph Coloring）**来解决优化问题。 考虑到课程聚焦于人际关系的社交网络，还将探讨**语言处理（Language Processing）**的重要性。在社交媒体上，人们发布想法和评论，仅仅知道帖文或评论是否存在不足够，理解其**语义**至关重要。 最后，本课程将回顾**社交网络分析**的基础概念与应用，特别是其在**学习分析（Learning Analytics）**领域的应用。你将获得操作、分析和可视化网络数据的实际技能。
Everything is connected: people, information, events and places, all the more so with the advent of online social media. A practical way of making sense of the tangle of connections is to analyze them as networks. In this course, we start with graph theory and extend our discussion to complex networks. Network analysis techniques are discussed in relevance to real world problems to arrive at interesting results. There are practical demonstrations of the theoretical concepts in Python using packages like NetworkX, Matplotlib for plotting and visualizing while Numpy and Pandas for reading and presenting data. Gephi is also discussed for performing different analytics on the data through its interface.You will learn how to prepare data and map these relationships to help you understand how people communicate and exchange information.It elaborates on link analysis using different techniques to determine the importance of a node e.g., centrality, prestige and particularly page rank algorithm. A particular emphasis is laid in understanding graph traversals, i.e., using the shortest path algorithms and solving optimization problems using graph coloring.Since we are considering social networks that is the network among human actors, therefore, it also enhances the importance of language processing which is often using by humans to socialize. On social media, we see people posting their thoughts, and sharing comments on others posts. Therefore, just knowing the presence or absence of a post or comment is not important, but we also need to use language processing techniques to understand the semantics of it.The course will review foundational concepts and applications of social network analysis in learning analytics. You will also learn how to manipulate, analyze, and visualize network data.
