Social Network Analysis(SNA) and Graph Analysis using Python

Learn and Use SNA in advance Machine learning

2.65 (28 reviews)
Udemy
platform
English
language
Data Science
category
308
students
4.5 hours
content
Feb 2022
last update
$49.99
regular price

What you will learn

1. The content (80% hands on and 20% theory) will prepare you to work independently on SNA projects

2. Learn - Basic, Intermediate and Advance concepts

3. Graph’s foundations (20 techniques)

4. Graph’s use cases (6 use cases)

5. Link Analysis (how Google search the best link/page for you)

6. Page Ranks

7. Hyperlink-Induced Topic Search (HITS; also known as hubs and authorities)

8. Node embedding

9. Recommendations using SNA (theory)

10. Management and monitoring of complex networks (theory)

11. How to use SNA for Data Analytics (theory)

Description

As practitioner of SNA, I am trying to bring many relevant topics under one umbrella in following topics so that it can be uses in advance machine learning areas.

1. The content (80% hands on and 20% theory) will prepare you to work independently on SNA projects

2. Learn - Basic, Intermediate and Advance concepts

3. Graph’s foundations (20 techniques)

4. Graph’s use cases (6 use cases)

5. Link Analysis (how Google search the best link/page for you)

6. Page Ranks

7. Hyperlink-Induced Topic Search (HITS; also known as hubs and authorities)

8. Node embedding

9. Recommendations using SNA (theory)

10. Management and monitoring of complex networks (theory)

11. How to use SNA for Data Analytics (theory)

Content

Introduction

Introduction
Installations , Technology , Folder structure

Graph's foundations

History of Graph Theory
Definitions of Graph
Graph foundations_Data Preparations
Graph foundations_Explore undirectional graph
Small world - Six degrees of separation
Small world - Six degrees of separation_code
Diameter - Transitivity - SubTree - Eccentricity - Closeness Centrality-Eigenve
Betweenness centrality - communities - cliques - Adjacency matrix
Directional Graph
sna_basic_seealsology_data

Use case: Airlines

Use case airlines data preparations
Density - Transitivity - Layouts - Diameter - Shortest paths
Degree - Subtree - Eccentricity - Closeness - Eigenvector - Betweenness - Commun
Few practical question and answer

Use case: Fraudulent network data analysis

Usecase_fraudulent_network_data_preprations
Transitivity - Closeness - Eigenvector - Betweenness - Communities - Directional
KMean clustering
Advance Statistics - Fraud score calculation
Supervised Analytics

Use case: Enron scandal

Enron_introduction and data loading
Clean and Prepare data for unidirectional graph
Density - Transitivity - Layouts - nx visualization
Degree - Subtree - Eccentricity - Closeness - Eigenvector - Betweenness - Commun
Class work - Please do yourself line by line

Extended features of Graph

Page Rank
Page Rank - code

Node Embedding

Node Embeddings - Pre requisites
Word embedding
Node Embedding - Definition and Methods
Node Embedding using Deep Walk
Node Embedding using Node2Vec

Miscellaneous

How to use SNA for Data Analytics

Screenshots

Social Network Analysis(SNA) and Graph Analysis using Python - Screenshot_01Social Network Analysis(SNA) and Graph Analysis using Python - Screenshot_02Social Network Analysis(SNA) and Graph Analysis using Python - Screenshot_03Social Network Analysis(SNA) and Graph Analysis using Python - Screenshot_04

Reviews

Abrielle
June 13, 2023
The course was very repetitive at the beginning, running the same code over and over again for different examples. Topics were mentioned, said they would be discussed later, and never brought back up. It was a lot of hand waving and not a lot of depth into the topics. It was good to have provided code, but providing code doesn't make a class hands-on. For hands-on, I would have expected exercise sections with review of a solution after. Overall, hard to follow.
Marko
December 28, 2021
Course is very hard to follow. Concept that are related to the SNA is not explained well, especially when it is needed to interpret results obtained in analysis. Python code is very very hard to follow. Most of the time code is only executed without explaining what this code actually do. All in all, course is hard to follow, and I didn't learn anything that I can use.
Shelly
December 5, 2020
The course was good, the tutor could have been a person who could have organized his /her thoughts and completed every topic effectively instead of sporadically jumping from one topic to other without providing closure.
Rabab
September 6, 2020
pros: new stuff ! + instructor responsive + most concepts with code cons: voice is not of high quality + problem with watching the video at speed

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3315306
udemy ID
7/9/2020
course created date
7/30/2020
course indexed date
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course submited by