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Applied Social Network Analysis in Python by University of Michigan on Coursera

sambhipiyush/Applied-Social-Network-Analysis-in-Python-University-of-Michigan

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Instructor(s) : Daniel Romero

About this course.

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.

• Recognize and categorize real world networks.
• Identify applications and important questions about networks that network science allows us to answer.
• Determine what type of network is best suited to model real networked data.
• Construct and manipulate networks of different types using different network classes and node and edge attributes in NetworkX.
• Define bipartite graphs and describe related algorithms such as graph projections.
• Manipulate bipartite graphs and related algorithms using NetworkX.

Module 1: Why Study Networks and Basics on NetworkX

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.

Graded: Module 1 Quiz Graded: Assignment 1 Submission

• Describe how distance measures can be used to identify central and peripheral nodes in networks and use networkX to identify central and peripheral nodes.
• Define connected components in directed and undirected graphs and use networkX to find them.
• Identify ways of measuring clustering in networks and distinguish the differences in the measures.
• Measure clustering in graphs in NetworkX.
• Identify different types of network attacks and relate them to real world examples.
• Define node and edge connectivity of a network and describe their implication to network robustness.
• Assess how resistant a network is to node and edge removal attacks, using networkX.

Module 2: Network Connectivity

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.

Graded: Module 2 Quiz Graded: Assignment 2 Submission

• Identify and define several network centrality measures.
• Describe the differences and similarities between several centrality measures.
• Measure the centrality of nodes in a network using NetworkX.
• Describe how network centrality measures can be used for real world applications.
• Apply centrality analysis to real world networks.

Module 3: Influence Measures and Network Centralization

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.

Graded: Module 3 Quiz Graded: Assignment 3 Submission

• Define the degree distribution of a network and use networkX to visualize it.
• Identify the typical properties of real, large networks such as power law like degree distribution, high clustering, and small average shortest paths.
• Describe the mechanics of the Preferential Attachment Model and identify the properties of the networks it generates.
• Describe the mechanics of the Small World Model and identify the properties of the networks it generates.
• Identify and describe several network measures that can be used for link prediction.
• Use NetworkX to create node pair features that are predictive of new edges.
• Use NetworkX to create node features that are predictive of node attributes in a real world setting.

Module 4: Network Evolution

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.

Graded: Module 4 Quiz Graded: Assignment 4 Submission

Grading and Assignments

Warm Regards, Piyush Sambhi Email: [email protected] Git URL: https://github.com/sambhipiyush

• Jupyter Notebook 99.1%
• Python 0.9%

Assignment 1 - Network Analysis

Introduction.

• In th first Part 2, you will replicate a well known network analysis, with different data and some twists.
• Data: The data is to be found at: https://github.com/SDS-AAU/SDS-master/tree/master/00_data/network_krackhard (Hint: You neet to download the raw data)

Data: What do I get?

Let the fun begin. You will analyze network datacollected from the managers of a high-tec company. This dataset, originating from the paper below, is widely used in research on organizational networks. Time to give it a shot as well. Krackhardt D. (1987). Cognitive social structures. Social Networks, 9, 104-134. The company manufactured high-tech equipment on the west coast of the United States and had just over 100 employees with 21 managers. Each manager was asked to whom do you go to for advice and who is your friend, to whom do you report was taken from company documents. Description

The dataset includes 4 files - 3xKrack-High-Tec and 1x High-Tec-Attributes. Krack-High-Tec includes the following three 21x3 text matrices:

• ADVICE, directed, binary
• FRIENDSHIP, directed, binary
• REPORTS_TO, directed, binary

Column 1 contains the ID of the ego (from where the edge starts), and column 2 the alter (to which the edge goes). Column 3 indicates the presence (=1) or absence (=0) of an edge.

High-Tec-Attributes includes one 21x4 valued matrix.

• ID: Numeric ID of the manager
• AGE: The managers age (in years)
• TENURE: The length of service or tenure (in years)
• LEVEL: The level in the corporate hierarchy (coded 1,2 and 3; 1 = CEO, 2 = Vice President, 3 = manager)
• DEPT: The department (coded 1,2,3,4 with the CEO in department 0, ie not in a department)

1. Create a network

• Generate network objects for the companies organizational structure (reports to), friendship, advice
• This networks are generated from the corresponding edgelists
• Also attach node characteristics from the corresponding nodelist

2. Analysis

Make a little analysis on:

A: Network level characteristics. Find the overal network level of:

• Transistivity (Clustering Coefficient)
• Reciprocity

… for the different networks. Describe and interpret the results. Answer the following questions:

• Are relationships like friendship and advice giving usually reciprocal?
• Are friends of your friends also your friends?
• Are the employees generally more likely to be in a friendship or advice-seeking relationship?

B: Node level characteristics: Likewise, find out:

• Who is most popular in the networks. Who is the most wanted friend, and advice giver?
• Are managers in higher hirarchy more popular as friend, and advice giver?

C: Relational Characteristics: Answer the following questions:

• Are managers from the same 1. department, or on the same 2. hirarchy, 3. age, or 4. tenuere more likely to become friends or give advice? (hint: assortiativity related)
• Are friends more likely to give each others advice?

3. Visualization

Everything goes. Show us some pretty and informative plots. Choose what to plot, and how, on your own. Interpret the results and share some insights.

Submission as PDF (notebook and output)

Submission: Thursday 7.10.2021 23:59:00. Peergrade.io

• R team :::: HERE ::::
• Py team :::: HERE ::::

Social Network Analysis

Note: This exam date is subjected to change based on seat availability. You can check final exam date on your hall ticket.

Page Visits

Course layout, books and references.

• Social Network Analysis, Tanmoy Chakraborty, Wiley, 2021
• Network Science, Albert-Lazzlo Barabasi
• Social Network Analysis: Methods and Applications, Stanley Wasserman, Katherine Faus

Instructor bio

Prof. Tanmoy Chakraborty

Course certificate.

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