000			02599nam a22002777a 4500
005			20231103130411.0
008			231103b |||||||| |||| 00| 0 eng d
020			_a9781800204492
028			_a: Technical Bureau _b: E/261, Shastri Nagar, Delhi. _c: TB2272 _d: 27/10/2023
082			_a006.31 STA
100			_aStamile, Claudio.
100			_a; Deusebio, Enrico.
100			_a; Marzullo, Aldo.
245			_aGraph machine learning _b: take graph data to the next level by applying machine learning techniques and algorithms. _c/Claudio Stamile.
260			_a, Birmingham _b: Packt Publishing Ltd. _c, 2021.
300			_axi, 338 p. _c: 26cm.
365			_b3899
505			_a Getting Started with Graphs Graph Machine Learning Machine Learning on Graphs Unsupervised Graph Learning Supervised Graph Learning Problems with Machine Learning on Graphs Social Network Graphs Text Analytics and Natural Language Processing Using Graphs Graph Analysis for Credit Card Transactions Building a Data-Driven Graph-Powered Application Novel Trends on Graphs
520			_a"Graph Machine Learning provides a new set of tools for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks. You will start with a brief introduction to graph theory and graph machine learning, understanding their potential. As you proceed, you will become well versed with the main machine learning models for graph representation learning: their purpose, how they work, and how they can be implemented in a wide range of supervised and unsupervised learning applications. You'll then build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. Moving ahead, you will cover real-world scenarios such as extracting data from social networks, text analytics, and natural language processing (NLP) using graphs and financial transaction systems on graphs. Finally, you will learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, before progressing to explore the latest trends on graphs. By the end of this machine learning book, you will have learned essential concepts of graph theory and all the algorithms and techniques used to build successful machine learning applications."
650			_aMachine learning
650			_aGraph theory
650			_aData processing
650			_aClaudio Stamile
700			_aClaudio Stamile.
942			_cBOOK
999			_c61093 _d61093