4946109Modern Graph Theory Algorithms with Pythonhttps://www.gandhi.com.mx/modern-graph-theory-algorithms-with-python-9781805120179/phttps://gandhi.vtexassets.com/arquivos/ids/4499045/image.jpg?v=638521873239570000665739MXNPackt PublishingInStock/Ebooks/4686422Modern Graph Theory Algorithms with Python665739https://www.gandhi.com.mx/modern-graph-theory-algorithms-with-python-9781805120179/phttps://gandhi.vtexassets.com/arquivos/ids/4499045/image.jpg?v=638521873239570000InStockMXN99999DIEbook20249781805120179_W3siaWQiOiI5NjRmYzEwOS1hN2EwLTQ5YjgtODNiZS1hNjkxNmU0NTUxZmUiLCJsaXN0UHJpY2UiOjczOSwiZGlzY291bnQiOjc0LCJzZWxsaW5nUHJpY2UiOjY2NSwiaW5jbHVkZXNUYXgiOnRydWUsInByaWNlVHlwZSI6Ildob2xlc2FsZSIsImN1cnJlbmN5IjoiTVhOIiwiZnJvbSI6IjIwMjQtMDYtMDdUMDA6MDA6MDBaIiwicmVnaW9uIjoiTVgiLCJpc1ByZW9yZGVyIjpmYWxzZX1d9781805120179_<p><b>Solve challenging and computationally intensive analytics problems by leveraging network science and graph algorithms</b></p><h2>Key Features</h2><ul><li>Learn how to wrangle different types of datasets and analytics problems into networks</li><li>Leverage graph theoretic algorithms to analyze data efficiently</li><li>Apply your skills on a variety of hands-on problems through case studies in Python</li></ul><h2>Book Description</h2>We are living in the age of big data, and scalable solutions are a necessity. Network science leverages the power of graph theory and flexible data structures to analyze big data at scale. This book guides readers through the basics of network science, shows them how to wrangle different types of data (such as spatial and time series data) into network structures, and introduces core tools from network science to analyze real-world case studies in Python. In the first sections, find out how to predict fake news spread, track pricing patterns in local markets, forecast stock market crashes, and stop epidemic spread. In the final section, learn about advanced tools in network science, such as creating and querying graph databases, classifying datasets with graph neural networks, and mining educational pathways for insight about student success. Case studies provide you with end-to-end examples of implementing what you learn in each chapter. By the end of the book, youll be well-equipped to wrangle your own datasets into network science problems and scale solutions with Python.<h2>What you will learn</h2><ul><li>Learn how to wrangle different types of data into networks</li><li>Explore common network science tools in Python</li><li>Discover how geometry impacts spreading processes on networks</li><li>Implement machine learning algorithms on network data key features</li><li>Build and query graph databases</li><li>Explore new frontiers in network science like quantum algorithms</li></ul><h2>Who this book is for</h2><p>If you are a researcher or industry professional analyzing data and curious about network science approaches to data, this book is for you. To maximize your learning, you should have basic knowledge of Python, including pandas and numpy, and some experience working with datasets. This book is also good for anyone interested in network science and learning how graph algorithms are used to solve science and engineering problems. R programmers may also find this book useful, as many algorithms also have R implementations. </p>...9781805120179_Packt Publishinglibro_electonico_9781805120179_9781805120179Franck KalalaInglésMéxico2024-06-07T00:00:00+00:00https://getbook.kobo.com/koboid-prod-public/packt-epub-f379881e-0488-4630-9ff8-098f4d1539e9.epub2024-06-07T00:00:00+00:00Packt Publishing