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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/python-social-network-analysis
课程评论：没有评论
课程名称：Python中的应用社会网络分析 概述：本课程通过使用NetworkX库的教程向学习者介绍网络分析。课程开始于对网络分析的理解，以及为什么我们可能会将现象建模为网络的动机。第二周介绍了连通性和网络的鲁棒性概念。第三周将探索测量网络中节点重要性或中心性的方式。最后一周将探讨网络随时间演变的过程，涵盖网络生成模型和链接预测问题。 适合先修课程：数据科学入门（Python），应用绘图、图表和数据呈现（Python），应用机器学习（Python）。 课程大纲： 1. **网络学习的意义与NetworkX基础** - 描述：第一模块介绍现实世界中不同类型的网络及其研究意义。学习网络的基本元素以及不同类型的网络，掌握如何使用NetworkX库表示和操作网络数据。作业将让你有机会使用NetworkX分析小型公司的员工网络数据集。 2. **网络连通性** - 描述：第二模块将教你如何基于节点之间的距离、可达性和路径冗余等度量分析网络的连通性。作业中，你将练习使用NetworkX计算中型制造公司员工电子邮件通信网络的连通性度量。 3. **影响力度量与网络中心化** - 描述：第三模块探讨如何测量网络中节点的重要性或中心性，使用度数、接近度、介数中心性、Page Rank以及中心枢纽和权威等度量。学习每种度量的假设、计算这些度量的算法以及NetworkX中可用的不同函数。在作业中，你将练习在实际场景中选择最合适的中心性度量。 4. **网络演变** - 描述：第四模块探讨网络随时间演变的过程，包括生成具有现实特征的网络的不同模型，如优先连接模型和小世界网络。你还将探索链接预测问题，了解可以预测一对未连接节点未来是否会连接的有用特征。作业将挑战你识别给定网络的生成模型，并结合课程中的不同概念，预测公司员工的薪水、职位和未来连接。 此课程将帮助学习者掌握网络分析的基础知识，并应用于实际的网络数据集分析中。
Name:Why Study Networks and Basics on NetworkX
Description:Module One introduces you to different types of networks in the real world and why we study them. You'll learn about the basic elements of networks, as well as different types of networks. You'll also learn how to represent and manipulate networked data using the NetworkX library. The assignment will give you an opportunity to use NetworkX to analyze a networked dataset of employees in a small company.
Name:Network Connectivity
Description:In Module Two you'll learn how to analyze the connectivity of a network based on measures of distance, reachability, and redundancy of paths between nodes. In the assignment, you will practice using NetworkX to compute measures of connectivity of a network of email communication among the employees of a mid-size manufacturing company.
Name:Influence Measures and Network Centralization
Description:In Module Three, you'll explore ways of measuring the importance or centrality of a node in a network, using measures such as Degree, Closeness, and Betweenness centrality, Page Rank, and Hubs and Authorities. You'll learn about the assumptions each measure makes, the algorithms we can use to compute them, and the different functions available on NetworkX to measure centrality. In the assignment, you'll practice choosing the most appropriate centrality measure on a real-world setting.
Name:Network Evolution
Description:In Module Four, you'll explore the evolution of networks over time, including the different models that generate networks with realistic features, such as the Preferential Attachment Model and Small World Networks. You will also explore the link prediction problem, where you will learn useful features that can predict whether a pair of disconnected nodes will be connected in the future. In the assignment, you will be challenged to identify which model generated a given network. Additionally, you will have the opportunity to combine different concepts of the course by predicting the salary, position, and future connections of the employees of a company using their logs of email exchanges.
This course will introduce the learner to network analysis through tutorials using the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem. This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.
