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Python for Graph and Network Analysis (Advanced Information and Knowledge Processing)

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This research monograph provides the means to learn the theory and practice of graph and network analysis using the Python programming language. The social network analysis techniques, included, will help readers to efficiently analyze social data from Twitter, Facebook, LiveJournal, GitHub and many others at three levels of depth: ego, group, and community. They will be able to analyse militant and revolutionary networks and candidate networks during elections. For instance, they will learn how the Ebola virus spread through communities.

Practically, the book is suitable for courses on social network analysis in all disciplines that use social methodology. In the study of social networks, social network analysis makes an interesting interdisciplinary research area, where computer scientists and sociologists bring their competence to a level that will enable them to meet the challenges of this fast-developing field. Computer scientists have the knowledge to parse and process data while sociologists have the experience that is required for efficient data editing and interpretation. Social network analysis has successfully been applied in different fields such as health, cyber security, business, animal social networks, information retrieval, and communications. 

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Computational Linguistics and Intelligent Text Processing: 12th International Conference, CICLing 2011
Computational Linguistics and Intelligent Text Processing: 12th International Conference, CICLing 2011

CICLing 2011 was the 12th Annual Conference on Intelligent Text Processing and Computational Linguistics. The CICLing conferences provide a wide-scope forum for the discussion of the art and craft of natural language processing research as well as the best practices in its applications.

This set of two books contains four invited...

Deep Learning with TensorFlow: Explore neural networks and build intelligent systems with Python, 2nd Edition
Deep Learning with TensorFlow: Explore neural networks and build intelligent systems with Python, 2nd Edition

Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of TensorFlow.

Key Features

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  • Explore deep neural...
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Hands-On Software Engineering with Python: Move beyond basic programming and construct reliable and efficient software with complex code

Explore various verticals in software engineering through high-end systems using Python

Key Features

  • Master the tools and techniques used in software engineering
  • Evaluates available database options and selects one for the final Central Office system-components
  • ...

Applied Deep Learning with Python: Use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions
Applied Deep Learning with Python: Use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions

A hands-on guide to deep learning that's filled with intuitive explanations and engaging practical examples

Key Features

  • Designed to iteratively develop the skills of Python users who don't have a data science background
  • Covers the key foundational concepts...
Machine Learning for Finance: Principles and practice for financial insiders
Machine Learning for Finance: Principles and practice for financial insiders

A guide to advances in machine learning for financial professionals, with working Python code

Key Features

  • Explore advances in machine learning and how to put them to work in financial industries
  • Clear explanation and expert discussion of how machine learning works, with...
Beginning Java Data Structures and Algorithms: Sharpen your problem solving skills by learning core computer science concepts in a pain-free manner
Beginning Java Data Structures and Algorithms: Sharpen your problem solving skills by learning core computer science concepts in a pain-free manner

Though your application serves its purpose, it might not be a high performer. Learn techniques to accurately predict code efficiency, easily dismiss inefficient solutions, and improve the performance of your application.

Key Features

  • Explains in detail different algorithms and data...
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