Social Network Analysis(SNA) and Graph Analysis using Python

Name: Social Network Analysis(SNA) and Graph Analysis using Python
Rating: 3.5 (35 reviews)

Learn and Use SNA in advance Machine learning

Created byShiv Onkar Deepak Kumar

Last updated 2/2022

English

What you'll 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)

Course content

8 sections • 34 lectures • 4h 16m total length

Introduction3:19
Installations , Technology , Folder structure1:46

History of Graph Theory3:11
Definitions of Graph6:37
Graph foundations_Data Preparations11:08
Graph foundations_Explore undirectional graph18:05
Small world - Six degrees of separation5:30
Small world - Six degrees of separation_code5:45
Diameter - Transitivity - SubTree - Eccentricity - Closeness Centrality-Eigenve8:30
Betweenness centrality - communities - cliques - Adjacency matrix9:51
Directional Graph3:31
Learn how directional graphs model asymmetric relations in social networks, when edge direction matters, and how multiple interactions imply connectivity, sometimes converted to undirected for analysis.
sna_basic_seealsology_data7:12

Use case airlines data preparations7:37
Explore preprocessing of airline data for graph analysis: download, clean, and select key columns, handle missing values, drop duplicates, and prepare a scalable dataset for social network analysis in Python.
Density - Transitivity - Layouts - Diameter - Shortest paths7:13
Degree - Subtree - Eccentricity - Closeness - Eigenvector - Betweenness - Commun10:40
Few practical question and answer4:59

Usecase_fraudulent_network_data_preprations8:25
Transitivity - Closeness - Eigenvector - Betweenness - Communities - Directional11:36
Explore transitivity, closeness, eigenvector, betweenness, communities, and directional aspects in social networks using Python, analyzing density and distances to reveal key structures.
KMean clustering9:58
Advance Statistics - Fraud score calculation9:23
Supervised Analytics14:08

Enron_introduction and data loading12:21
Clean and Prepare data for unidirectional graph10:10
Learn to clean and prepare email data for a unidirectional graph by grouping records, creating a new column, and filtering for clear social network analysis and graph analysis.
Density - Transitivity - Layouts - nx visualization5:40
Degree - Subtree - Eccentricity - Closeness - Eigenvector - Betweenness - Commun7:18
Class work - Please do yourself line by line2:34

Requirements

Awareness of Python
Awareness of Machine Learning using Python

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)

Who this course is for:

Anyone who want to Learn and Apply SNA using Python
Anyone who want to Learn advance Machine Learning

Social Network Analysis(SNA) and Graph Analysis using Python

What you'll learn

Explore related topics

Course content

Introduction2 lectures • 5min

Graph's foundations10 lectures • 1hr 19min

Use case: Airlines4 lectures • 30min

Use case: Fraudulent network data analysis5 lectures • 54min

Use case: Enron scandal5 lectures • 38min

Extended features of Graph2 lectures • 12min

Node Embedding5 lectures • 34min

Miscellaneous1 lecture • 3min

Requirements

Description

Who this course is for: