Filter
Add to wishlist

Bayesian Network Technologies: Applications and Graphical Models eBook

$15.00

  • Delivery: Can be download immediately after purchasing. For new customer, we need process for verification from 30 mins to 24 hours.
  • Version: PDF/EPUB. If you need another version, please Contact us
  • Quality: Full page, full content, high quality images, searchable text and you can print it.
  • Compatible Devices: Can be read on any devices (Kindle, NOOK, Android/IOS devices, Windows, MAC,..).
  • e-Book Features: Purchase and read your book immediately, access your eTextbook anytime and anywhere, unlimited download and share with friends.
  • Note: If you do not receive the download link within 15 minutes of your purchase, please Contact us. Thank you!
  • 5% off all orders over $25 with code:: 5OFF2024
  • 10% off all orders over $50 with code: 10OFF2024
SKU: 9781599041438 Categories: ,

‘Network’ is a heavily overloaded term, so that ‘network analysis’ means different things to different people. Specific forms of network analysis are used in the study of diverse structures such as the Internet, interlocking directorates, transportation systems, epidemic spreading, metabolic pathways, the Web graph, electrical circuits, project plans, and so on. There is, however, a broad methodological foundation which is quickly becoming a prerequisite for researchers and practitioners working with network models.

From a computer science perspective, network analysis is applied graph theory. Unlike standard graph theory books, the content of this book is organized according to methods for specific levels of analysis (element, group, network) rather than abstract concepts like paths, matchings, or spanning subgraphs. Its topics therefore range from vertex centrality to graph clustering and the evolution of scale-free networks.

In 15 coherent chapters, this monograph-like tutorial book introduces and surveys the concepts and methods that drive network analysis, and is thus the first book to do so from a methodological perspective independent of specific application areas.