book

Network Science with Python

Name: Network Science with Python
Author: David Knickerbocker
ISBN: 9781801073691

by David Knickerbocker

February 2023

Intermediate to advanced

414 pages

9h 24m

English

Packt Publishing

Read now

Unlock full access

Network Science with Python
AcknowledgementsContributorsAbout the authorAbout the reviewers
Preface
Who this book is forWhat this book coversDownload the example code filesConventions usedGet in touchShare Your ThoughtsDownload a free PDF copy of this book
Part 1: Getting Started with Natural Language Processing and Networks
Chapter 1: Introducing Natural Language Processing
Technical requirementsWhat is NLP?Why NLP in a network analysis book?A very brief history of NLPHow has NLP helped me?Simple text analysisCommunity sentiment analysisAnswer previously unanswerable questionsSafety and securityCommon uses for NLPTrue/False – Presence/AbsenceRegular expressions (regex)Word countsSentiment analysisInformation extractionCommunity detectionClusteringAdvanced uses of NLPChatbots and conversational agentsLanguage modelingText summarizationTopic discovery and modelingText-to-speech and speech-to-text conversionMTPersonal assistantsHow can a beginner get started with NLP?Start with a simple ideaAccounts that post most frequentlyAccounts mentioned most frequentlyTop 10 data science hashtagsAdditional questions or action items from simple analysisSummary
Chapter 2: Network Analysis
The confusion behind networksWhat is this network stuff?Graph theorySocial network analysisNetwork scienceResources for learning about network analysisNotebook interfacesIDEsNetwork datasetsKaggle datasetsNetworkX and scikit-network graph generatorsCreating your own datasetsNetworkX and articlesCommon network use casesMapping production dataflowMapping community interactionsMapping literary social networksMapping historical social networksMapping languageMapping dark networksMarket researchFinding specific contentCreating ML training dataAdvanced network use casesGraph MLRecommendation systemsGetting started with networksExample – K-pop implementationSummaryFurther reading
Chapter 3: Useful Python Libraries
Technical requirementsUsing notebooksData analysis and processingpandasNumPyData visualizationMatplotlibSeabornPlotlyNLPNatural Language ToolkitSetupStarter functionalityDocumentationspaCyNetwork analysis and visualizationNetworkXscikit-networkMLscikit-learnKarate ClubspaCy (revisited)Summary
Part 2: Graph Construction and Cleanup
Chapter 4: NLP and Network Synergy
Technical requirementsWhy are we learning about NLP in a network book?Asking questions to tell a storyIntroducing web scrapingIntroducing BeautifulSoupLoading and scraping data with BeautifulSoupChoosing between libraries, APIs, and source dataUsing NLTK for PoS taggingUsing spaCy for PoS tagging and NERSpaCy PoS taggingSpaCy NERConverting entity lists into network dataConverting network data into networksDoing a network visualization spot checkAdditional NLP and network considerationsData cleanupComparing PoS tagging and NERScraping considerationsSummary
Chapter 5: Even Easier Scraping!
Technical requirementsWhy cover Requests and BeautifulSoup?Introducing Newspaper3kWhat is Newspaper3k?What are Newspaper3k’s uses?Getting started with Newspaper3kScraping all news URLs from a websiteScraping a news story from a websiteScraping nicely and blending inConverting text into network dataEnd-to-end Network3k scraping and network visualizationIntroducing the Twitter Python LibraryWhat is the Twitter Python Library?What are the Twitter Library’s uses?What data can be harvested from Twitter?Getting Twitter API accessAuthenticating with TwitterScraping user tweetsScraping user followingScraping user followersScraping using search termsConverting Twitter tweets into network dataEnd-to-end Twitter scrapingSummary
Chapter 6: Graph Construction and Cleaning
Technical requirementsCreating a graph from an edge listTypes of graphsSummarizing graphsListing nodesRemoving nodesQuick visual inspectionAdding nodesAdding edgesRenaming nodesRemoving edgesPersisting the networkSimulating an attackSummary

Part 3: Network Science and Social Network Analysis
Chapter 7: Whole Network Analysis
Technical requirementsCreating baseline WNA questionsRevised SNA questionsSocial network analysis revisitedWNA in actionLoading data and creating networksNetwork size and complexityNetwork visualization and thoughtsImportant nodesDegreesDegree centralityBetweenness centralityCloseness centralityPageRankEdge centralitiesComparing centralitiesVisualizing subgraphsInvestigating islands and continents – connected componentsCommunitiesBridgesUnderstanding layers with k_core and k_coronak_corek_coronaChallenge yourself!Summary
Chapter 8: Egocentric Network Analysis
Technical requirementsEgocentric network analysisUses for egocentric network analysisExplaining the analysis methodologyInvestigating ego nodes and connectionsEgo 1 – ValjeanEgo 2 – MariusEgo 3 – GavrocheEgo 4 – JolyInsights between egosIdentifying other research opportunitiesSummary
Chapter 9: Community Detection
Technical requirementsIntroducing community detectionGetting started with community detectionExploring connected componentsUsing the Louvain methodHow does it work?The Louvain method in action!Using label propagationHow does it work?Label propagation in action!Using the Girvan-Newman algorithmHow does it work?Girvan-Newman algorithm in action!Other approaches to community detectionSummary
Chapter 10: Supervised Machine Learning on Network Data
Technical requirementsIntroducing MLBeginning with MLData preparation and feature engineeringDegreesClusteringTrianglesBetweenness centralityCloseness centralityPageRankAdjacency matrixMerging DataFramesAdding labelsSelecting a modelPreparing the dataTraining and validating the modelModel insightsOther use casesSummary
Chapter 11: Unsupervised Machine Learning on Network Data
Technical requirementsWhat is unsupervised ML?Introducing Karate ClubNetwork science optionsUses of unsupervised ML on network dataCommunity detectionGraph embeddingsConstructing a graphCommunity detection in actionSCDGraph embeddings in actionFEATHERNodeSketchRandNEOther modelsUsing embeddings in supervised MLPros and consLoss of explainability and insightsAn easier workflow for classification and clusteringSummary
Index
Why subscribe?
Other Books You May EnjoyPackt is searching for authors like youShare Your ThoughtsDownload a free PDF copy of this book

Overview

Dive into the exciting field of network science with "Network Science with Python" by David Knickerbocker. This book provides a thorough and practical exploration into using Python to analyze, visualize, and understand complex networks in social science, data science, and beyond. You'll learn step-by-step how to harness powerful libraries and techniques to draw insights from networked data.

What this Book will help me do

Develop skills in using Python for analyzing and visualizing network data.
Understand both foundational and advanced network analysis methods.
Apply concepts of network science and social network analysis to real-world problems.
Learn to construct, clean, and model networks for data science applications.
Discover how network data integration can enhance machine learning projects.

Author(s)

David Knickerbocker is an educator and practitioner in data science and network analysis. With a strong background in Python programming and expertise in social network analysis, David has helped transform data into actionable insights across different domains. His teaching philosophy emphasizes practical applications and step-by-step learning, making complex topics accessible and engaging.

Who is it for?

This book is ideal for data scientists, Python programmers, and social scientists interested in exploring network science. It's also suitable for students and professionals who already have an intermediate understanding of Python and are looking to expand their skillset into network analysis. Whether you are a software engineer or a researcher in the social sciences, this book will provide new perspectives and practical, hands-on learning opportunities.

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.

Julian F.

Head of Cybersecurity

I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.

Addison B.

Field Engineer

I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.

Amir M.

Data Platform Tech Lead

I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.

Mark W.

Embedded Software Engineer

Network Science with Python and NetworkX Quick Start Guide

Publisher Resources

ISBN: 9781801073691

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills