Complex Network Analysis in Python: Recognize - Construct - Visualize - Analyze - Interpret (Paperback)

Dmitry Zinoviev

  • 出版商: Pragmatic Bookshelf
  • 出版日期: 2018-02-27
  • 定價: $1,260
  • 售價: 9.5$1,197
  • 語言: 英文
  • 頁數: 262
  • 裝訂: Paperback
  • ISBN: 1680502697
  • ISBN-13: 9781680502695
  • 相關分類: Python程式語言
  • 立即出貨 (庫存 < 4)

買這商品的人也買了...

相關主題

商品描述

Construct, analyze, and visualize networks with networkx, a Python language module. Network analysis is a powerful tool you can apply to a multitude of datasets and situations. Discover how to work with all kinds of networks, including social, product, temporal, spatial, and semantic networks. Convert almost any real-world data into a complex network--such as recommendations on co-using cosmetic products, muddy hedge fund connections, and online friendships. Analyze and visualize the network, and make business decisions based on your analysis. If you're a curious Python programmer, a data scientist, or a CNA specialist interested in mechanizing mundane tasks, you'll increase your productivity exponentially.

Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience.

Starting with simple networks, convert real-life and synthetic network graphs into networkx data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. Get familiar with presentation-quality network visualization tools, both programmable and interactive--such as Gephi, a CNA explorer. Adapt the patterns from the case studies to your problems. Explore big networks with NetworKit, a high-performance networkx substitute. Each part in the book gives you an overview of a class of networks, includes a practical study of networkx functions and techniques, and concludes with case studies from various fields, including social networking, anthropology, marketing, and sports analytics.

Combine your CNA and Python programming skills to become a better network analyst, a more accomplished data scientist, and a more versatile programmer.

What You Need:

You will need a Python 3.x installation with the following additional modules: Pandas (>=0.18), NumPy (>=1.10), matplotlib (>=1.5), networkx (>=1.11), python-louvain (>=0.5), NetworKit (>=3.6), and generalizesimilarity. We recommend using the Anaconda distribution that comes with all these modules, except for python-louvain, NetworKit, and generalizedsimilarity, and works on all major modern operating systems.

商品描述(中文翻譯)

使用Python語言模組networkx,構建、分析和可視化網絡。網絡分析是一種強大的工具,可應用於多種數據集和情境。了解如何處理各種網絡,包括社交、產品、時間、空間和語義網絡。將幾乎任何現實世界的數據轉換為複雜網絡,例如共同使用化妝品的推薦、混濁的對沖基金關係和網絡友誼。分析和可視化網絡,並根據分析結果做出業務決策。如果你是一個好奇的Python程序員、數據科學家或對機械化乏味任務感興趣的CNA專家,你的生產力將成倍增加。

以前,複雜網絡分析是通過手工或非可編程的網絡分析工具完成的,但現在不再如此!現在你可以使用Python自動化和編程這些任務。複雜網絡是連接的項目、詞語、概念或人的集合。通過探索它們的結構和個別元素,我們可以了解它們的含義、演化和韌性。

從簡單的網絡開始,將現實生活和合成網絡圖轉換為networkx數據結構。研究更複雜的網絡,學習處理中心性計算、區塊建模以及點擊和社區檢測的更強大工具。熟悉高質量的網絡可視化工具,包括可編程和交互式的工具,如CNA探索器Gephi。將案例研究中的模式應用於你的問題。使用NetworKit,一個高性能的networkx替代品,探索大型網絡。書中的每一部分都對一類網絡進行概述,包括networkx函數和技術的實際研究,並以社交網絡、人類學、市場營銷和體育分析等各個領域的案例研究作為結尾。

結合你的CNA和Python編程技能,成為一名更好的網絡分析師、更有成就的數據科學家和更多才多藝的程序員。

所需材料:
你需要安裝Python 3.x,並安裝以下附加模組:Pandas(>=0.18)、NumPy(>=1.10)、matplotlib(>=1.5)、networkx(>=1.11)、python-louvain(>=0.5)、NetworKit(>=3.6)和generalizesimilarity。我們建議使用Anaconda發行版,該發行版包含所有這些模組,除了python-louvain、NetworKit和generalizesimilarity,並且適用於所有主要現代操作系統。