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

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 commu...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Knickerbocker, David (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, UK : Packt Publishing Limited, 2023.
Temas:
Python (Computer program language)
Data mining.
Computer networks > Management.
Electronic data processing > Distributed processing.
Application software > Development.
Information visualization.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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)

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