Network science with Python : explore the networks around us using network science, social network analysis, and machine learning / David Knickerbocker.
2023
QA76.73.P98 K55 2023
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Details
Title
Network science with Python : explore the networks around us using network science, social network analysis, and machine learning / David Knickerbocker.
ISBN
9781801075213 electronic book
1801075212 electronic book
1801073694
9781801073691
1801075212 electronic book
1801073694
9781801073691
Published
Birmingham, UK : Packt Publishing Limited, 2023.
Language
English
Description
1 online resource
Call Number
QA76.73.P98 K55 2023
System Control No.
(OCoLC)1371686676
Summary
Discover the use of graph networks to develop a new approach to data science using theoretical and practical methods with this expert guide using Python, printed in color Key Features Create networks using data points and information Learn to visualize and analyze networks to better understand communities Explore the use of network data in both - supervised and unsupervised machine learning projects Purchase of the print or Kindle book includes a free PDF eBook Book Description Network analysis is often taught with tiny or toy data sets, leaving you with a limited scope of learning and practical usage. Network Science with Python helps you extract relevant data, draw conclusions and build networks using industry-standard - practical data sets. You'll begin by learning the basics of natural language processing, network science, and social network analysis, then move on to programmatically building and analyzing networks. You'll get a hands-on understanding of the data source, data extraction, interaction with it, and drawing insights from it. This is a hands-on book with theory grounding, specific technical, and mathematical details for future reference. As you progress, you'll learn to construct and clean networks, conduct network analysis, egocentric network analysis, community detection, and use network data with machine learning. You'll also explore network analysis concepts, from basics to an advanced level. By the end of the book, you'll be able to identify network data and use it to extract unconventional insights to comprehend the complex world around you. What you will learn Explore NLP, network science, and social network analysis Apply the tech stack used for NLP, network science, and analysis Extract insights from NLP and network data Generate personalized NLP and network projects Authenticate and scrape tweets, connections, the web, and data streams Discover the use of network data in machine learning projects Who this book is for Network Science with Python demonstrates how programming and social science can be combined to find new insights. Data scientists, NLP engineers, software engineers, social scientists, and data science students will find this book useful. An intermediate level of Python programming is a prerequisite. Readers from both - social science and programming backgrounds will find a new perspective and add a feather to their hat.
Formatted Contents Note
Cover
Title Page
Copyright and Credits
Acknowledgements
Contributors
Table of Contents
Preface
Part 1: Getting Started with Natural Language Processing and Networks
Chapter 1: Introducing Natural Language Processing
Technical requirements
What is NLP?
Why NLP in a network analysis book?
A very brief history of NLP
How has NLP helped me?
Simple text analysis
Community sentiment analysis
Answer previously unanswerable questions
Safety and security
Common uses for NLP
True/False
Presence/Absence
Regular expressions (regex)
Word counts
Sentiment analysis
Information extraction
Community detection
Clustering
Advanced uses of NLP
Chatbots and conversational agents
Language modeling
Text summarization
Topic discovery and modeling
Text-to-speech and speech-to-text conversion
MT
Personal assistants
How can a beginner get started with NLP?
Start with a simple idea
Accounts that post most frequently
Accounts mentioned most frequently
Top 10 data science hashtags
Additional questions or action items from simple analysis
Summary
Chapter 2: Network Analysis
The confusion behind networks
What is this network stuff?
Graph theory
Social network analysis
Network science
Resources for learning about network analysis
Notebook interfaces
IDEs
Network datasets
Kaggle datasets
NetworkX and scikit-network graph generators
Creating your own datasets
NetworkX and articles
Common network use cases
Mapping production dataflow
Mapping community interactions
Mapping literary social networks
Mapping historical social networks
Mapping language
Mapping dark networks
Market research
Finding specific content
Creating ML training data
Advanced network use cases
Graph ML
Recommendation systems
Getting started with networks
Example
K-pop implementation
Summary
Further reading
Chapter 3: Useful Python Libraries
Technical requirements
Using notebooks
Data analysis and processing
pandas
NumPy
Data visualization
Matplotlib
Seaborn
Plotly
NLP
Natural Language Toolkit
Setup
Starter functionality
Documentation
spaCy
Network analysis and visualization
NetworkX
scikit-network
ML
scikit-learn
Karate Club
spaCy (revisited)
Title Page
Copyright and Credits
Acknowledgements
Contributors
Table of Contents
Preface
Part 1: Getting Started with Natural Language Processing and Networks
Chapter 1: Introducing Natural Language Processing
Technical requirements
What is NLP?
Why NLP in a network analysis book?
A very brief history of NLP
How has NLP helped me?
Simple text analysis
Community sentiment analysis
Answer previously unanswerable questions
Safety and security
Common uses for NLP
True/False
Presence/Absence
Regular expressions (regex)
Word counts
Sentiment analysis
Information extraction
Community detection
Clustering
Advanced uses of NLP
Chatbots and conversational agents
Language modeling
Text summarization
Topic discovery and modeling
Text-to-speech and speech-to-text conversion
MT
Personal assistants
How can a beginner get started with NLP?
Start with a simple idea
Accounts that post most frequently
Accounts mentioned most frequently
Top 10 data science hashtags
Additional questions or action items from simple analysis
Summary
Chapter 2: Network Analysis
The confusion behind networks
What is this network stuff?
Graph theory
Social network analysis
Network science
Resources for learning about network analysis
Notebook interfaces
IDEs
Network datasets
Kaggle datasets
NetworkX and scikit-network graph generators
Creating your own datasets
NetworkX and articles
Common network use cases
Mapping production dataflow
Mapping community interactions
Mapping literary social networks
Mapping historical social networks
Mapping language
Mapping dark networks
Market research
Finding specific content
Creating ML training data
Advanced network use cases
Graph ML
Recommendation systems
Getting started with networks
Example
K-pop implementation
Summary
Further reading
Chapter 3: Useful Python Libraries
Technical requirements
Using notebooks
Data analysis and processing
pandas
NumPy
Data visualization
Matplotlib
Seaborn
Plotly
NLP
Natural Language Toolkit
Setup
Starter functionality
Documentation
spaCy
Network analysis and visualization
NetworkX
scikit-network
ML
scikit-learn
Karate Club
spaCy (revisited)
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