Social Network Analysis

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Taimoor khan

2:47:38

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1. Introduction

1.1 sna-introduction.pptx

1. Introduction.mp4

02:07

2. Graphs and Networks.mp4

04:09

3.1 2. Complex Networks.pptx

3. Complex Networks.mp4

05:16

4. Instructor.mp4

01:06

2. Graph Theory Types of Graphs

1. Simple Graphs.mp4

03:26

2. Directed Graphs.mp4

02:13

3. Weighted Graphs.mp4

01:45

4. Cyclic and Acyclic Graphs.mp4

02:36

5. Complete and Null Graphs.mp4

01:55

6. Subgraphs, Edge-disjoint Subgraphs.mp4

02:30

7. Connected, Disconnected Graphs.mp4

01:45

8. Isolated, Pendant Vertices.mp4

00:49

9. Bipartite, Multipartite Graphs.mp4

02:15

3. Graph Traversals with Dijkstra Shortest Path Algorithm

1. Shortest Path Algorithm Applications.mp4

02:58

2. Assumptions with Dijkstras Shortest Path Algorithm.mp4

03:11

3. Dijkstra Algorithm Working.mp4

04:12

4. Undirected Graph Example with Dijkstra Algorithm.mp4

10:54

5. Directed Graph Example with Dijkstra Algorithm.mp4

03:15

6. Computational Cost of Dijkstra Algorithm.mp4

03:44

4. Graph Traversals with Bellman Ford Algorithm

1. Bellman-Ford Shortest Path Algorithm.mp4

04:19

2. Example with Bellman-Ford Algorithm.mp4

04:27

3. Assumption with Bellman-Ford Algorithm.mp4

01:35

4. Computational Cost of Bellman-Ford Algorithm.mp4

01:27

5. Comparison of Shortest Path Algorithms.mp4

03:53

6. Identifying Negative Cycles in a Graph.mp4

02:52

5. Greedy Graph Coloring for Optimization

1. Graph Coloring.mp4

04:03

2. Graph Coloring Applications.mp4

02:16

3. Greedy Graph Coloring Algorithm.mp4

02:41

4. Example with Greedy Graph Coloring Algorithm.mp4

02:18

5. Limitation of Greedy Graph Coloring.mp4

04:27

6. Kempe Graph Coloring for Optimization

1. Kempe Graph Coloring.mp4

06:28

2. Example with Kempe Graph Coloring.mp4

02:28

3. Complex Example with Kempe Graph Coloring.mp4

03:52

7. Link Analysis

1. Introduction to Link Analysis.mp4

07:37

2. Centrality based Link Analysis.mp4

04:23

3. Closeness Centrality.mp4

07:01

4. Betweenness Centrality.mp4

03:39

5. Concluding Centrality.mp4

01:46

6. Degree and Proximity Prestige.mp4

06:17

7. PageRank Algorithm.mp4

14:28

8. Evaluating Basic PageRank.mp4

08:35

9. Evaluating Improved PageRank Algorithms.mp4

06:40

Description

Graph theory and complex network analysis in static and dynamic setup

What You'll Learn?

Apply the basics of social network analysis at node level, sub-graph level and graph level
Analyze social context in graphs
Conduct SNA on data collected in a learning setting
Design a research study using relational data

Who is this for?

computer and social scientists

What You Need to Know?

The theory is covered from very basics however for practicals knowledge of Python programming is requried

More details

Description
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 whileÂ 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. AÂ 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.
Who this course is for:
computer and social scientists

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Data Science

Taimoor khan

Instructor's Courses

I am a researcher and an academician since 2011, and have a background of professional software development for around 3 years. As an Assistant Professor in Computer Science faculty I have taught various courses to undergraduate and graduate students. I am particularly interested in courses related to software design and development, databases, artificial intelligence, machine learning and data mining etc.My PhD research is related to data science and computational linguistics, having worked with large-scale textual data for building knowledge-based systems that are adaptive and evolve with the growing needs without having to explicitly trained for a specific scenario. I have published papers in internationally recognized journals and conferences where we proposed solutions to real-world data analysis issues. I have supervised tens of projects that offered software based solutions for social content analytics, recommendations and tracking evolving public interests.

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