دانلود کتاب الگوریتم های مدرن تئوری گراف با پایتون

Hello there! Network science combines the power of analytics with the deep theoretical tools of graph theory to solve difficult problems in data analytics. This empowers researchers and industry engineers/ data scientists to analyze data at scale and reframe intractable analytics problems to produce powerful insights into problems and predictions about system behaviors, including biological, physical, and social systems of interest. There are many important applications of network science today, including these: • Social network data • Spatial data • Time series data • Spatiotemporal data • More advanced data structures, such as ontologies or hypergraphs This book gives a brief overview of social network applications and focuses on the cutting edge of network science applications to areas of data science, such as transportation logistics, conversation, public health, linguistics, and education. By the end of your journey, you’ll be able to frame your own data problem within the framework of network science to derive insights and tackle difficult problems in your field. We will provide the necessary mathematical background as we dive into practical examples and code related to our work in academia and industry over the past decades, including work on predicting Ebola outbreaks, forecasting food price volatility, modeling genetic and linguistic relationships, and mining social networks for insights into social tie formation. As the world faces food shortages, public health crises, economic inequality, supply chain breakdowns, and environmental crises, network science will play an important role in big data analytics for social good. This book is for you if you are working with data. To get the most out of the book, you should have some familiarity with Python, particularly the pandas and numpy packages. In addition, some familiarity with data analytics is assumed, though the network science tools and problems we tackle are built from scratch for readers without a background in those problems or methods. Network science has a rich history in many scientific disciplines, including epidemiology, biomedical engineering, sociology, genetics, environmental science, particle physics, computer science, and economics. Its foundations in graph theory influence research in many areas of pure and applied mathematics as well. Anyone in the fields of science, technology, engineering, and mathematics can benefit from network science’s toolset and approach to problem-solving.