Description
Social network analysis is used to investigate the inter-relationship between entities. Examples of network structures, include: social media networks, friendship networks and collaboration networks.
This book provides a quick start guide to network analysis and visualization in R.
You'll learn, how to:
- Create static and interactive network graphs using modern R packages.
- Change the layout of network graphs.
- Detect important or central entities in a network graph.
- Detect community (or cluster) in a network.
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This section contains best data science and self-development resources to help you on your path.
Books - Data Science
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- Practical Guide to Cluster Analysis in R by A. Kassambara (Datanovia)
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- GGPlot2 Essentials for Great Data Visualization in R by A. Kassambara (Datanovia)
- Network Analysis and Visualization in R by A. Kassambara (Datanovia)
- Practical Statistics in R for Comparing Groups: Numerical Variables by A. Kassambara (Datanovia)
- Inter-Rater Reliability Essentials: Practical Guide in R by A. Kassambara (Datanovia)
Others
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham & Garrett Grolemund
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurelien Géron
- Practical Statistics for Data Scientists: 50 Essential Concepts by Peter Bruce & Andrew Bruce
- Hands-On Programming with R: Write Your Own Functions And Simulations by Garrett Grolemund & Hadley Wickham
- An Introduction to Statistical Learning: with Applications in R by Gareth James et al.
- Deep Learning with R by François Chollet & J.J. Allaire
- Deep Learning with Python by François Chollet
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