Network Science with Python: Explore the networks around us using network science, social network analysis, and machine learning

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)
	Summary
Part 2: Graph Construction and Cleanup
Chapter 4: NLP and Network Synergy
	Technical requirements
	Why are we learning about NLP in a network book?
	Asking questions to tell a story
	Introducing web scraping
		Introducing BeautifulSoup
		Loading and scraping data with BeautifulSoup
	Choosing between libraries, APIs, and source data
	Using NLTK for PoS tagging
	Using spaCy for PoS tagging and NER
		SpaCy PoS tagging
		SpaCy NER
	Converting entity lists into network data
	Converting network data into networks
	Doing a network visualization spot check
	Additional NLP and network considerations
		Data cleanup
		Comparing PoS tagging and NER
		Scraping considerations
	Summary
Chapter 5: Even Easier Scraping!
	Technical requirements
	Why cover Requests and BeautifulSoup?
		Introducing Newspaper3k
		What is Newspaper3k?
		What are Newspaper3k’s uses?
	Getting started with Newspaper3k
		Scraping all news URLs from a website
		Scraping a news story from a website
		Scraping nicely and blending in
		Converting text into network data
		End-to-end Network3k scraping and network visualization
	Introducing the Twitter Python Library
		What is the Twitter Python Library?
		What are the Twitter Library’s uses?
		What data can be harvested from Twitter?
		Getting Twitter API access
		Authenticating with Twitter
		Scraping user tweets
		Scraping user following
		Scraping user followers
		Scraping using search terms
		Converting Twitter tweets into network data
		End-to-end Twitter scraping
	Summary
Chapter 6: Graph Construction and Cleaning
	Technical requirements
	Creating a graph from an edge list
		Types of graphs
		Summarizing graphs
	Listing nodes
	Removing nodes
	Quick visual inspection
	Adding nodes
		Adding edges
	Renaming nodes
	Removing edges
	Persisting the network
	Simulating an attack
	Summary
Part 3: Network Science and Social Network Analysis
Chapter 7: Whole Network Analysis
	Technical requirements
	Creating baseline WNA questions
		Revised SNA questions
		Social network analysis revisited
	WNA in action
		Loading data and creating networks
		Network size and complexity
		Network visualization and thoughts
		Important nodes
		Degrees
		Degree centrality
		Betweenness centrality
		Closeness centrality
		PageRank
		Edge centralities
	Comparing centralities
	Visualizing subgraphs
	Investigating islands and continents – connected components
		Communities
		Bridges
	Understanding layers with k_core and k_corona
		k_core
		k_corona
	Challenge yourself!
	Summary
Chapter 8: Egocentric Network Analysis
	Technical requirements
	Egocentric network analysis
		Uses for egocentric network analysis
		Explaining the analysis methodology
	Investigating ego nodes and connections
		Ego 1 – Valjean
		Ego 2 – Marius
		Ego 3 – Gavroche
		Ego 4 – Joly
		Insights between egos
	Identifying other research opportunities
	Summary
Chapter 9: Community Detection
	Technical requirements
	Introducing community detection
	Getting started with community detection
	Exploring connected components
	Using the Louvain method
		How does it work?
		The Louvain method in action!
	Using label propagation
		How does it work?
		Label propagation in action!
	Using the Girvan-Newman algorithm
		How does it work?
		Girvan-Newman algorithm in action!
	Other approaches to community detection
	Summary
Chapter 10: Supervised Machine Learning on Network Data
	Technical requirements
	Introducing ML
	Beginning with ML
	Data preparation and feature engineering
		Degrees
		Clustering
		Triangles
		Betweenness centrality
		Closeness centrality
		PageRank
		Adjacency matrix
		Merging DataFrames
		Adding labels
	Selecting a model
	Preparing the data
	Training and validating the model
	Model insights
	Other use cases
	Summary
Chapter 11: Unsupervised Machine Learning on Network Data
	Technical requirements
	What is unsupervised ML?
	Introducing Karate Club
	Network science options
	Uses of unsupervised ML on network data
		Community detection
		Graph embeddings
	Constructing a graph
	Community detection in action
		SCD
	Graph embeddings in action
		FEATHER
		NodeSketch
		RandNE
		Other models
	Using embeddings in supervised ML
		Pros and cons
		Loss of explainability and insights
		An easier workflow for classification and clustering
	Summary
Index
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