Python for graph and network analysis

• 作者： Al-Taie, Mohammed Zuhair, author.
• 其他作者：
  • Kadry, Seifedine, author.
  • SpringerLink (Online service)
• 其他題名：
  • Advanced information and knowledge processing.
• 出版： Cham : Springer International Publishing :Imprint: Springer
• 叢書名： Advanced information and knowledge processing,
• 主題： Python (Computer program language) , Graph theory--Data processing. , Quantitative research. , Online social networks--Data processing. , Computer Science. , System Performance and Evaluation. , Computer Appl. in Social and Behavioral Sciences. , Information Systems Applications (incl. Internet) , Python.
• ISBN： 9783319530048 (electronic bk.) 、 9783319530031 (paper)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: Theoretical Concepts of Network Analysis -- Network Basics -- Graph Theory -- Social Networks -- Node-Level Analysis -- Group-Level Analysis -- Network-Level Analysis -- Information Diffusion in Social Networks -- Appendix A: Python Tutorial -- Appendix B: NetworkX Tutorial.
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• 摘要註: 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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