Graph Algorithms for Data Science MEAP V08

Description

Graphs are the natural way to understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with practical examples and concrete advice on implementation and deployment. In Graph Algorithms for Data Science you will learn: Labeled-property graph modeling Constructing a graph from structured data such as CSV or SQL NLP techniques to construct a graph from unstructured data Cypher query language syntax to manipulate data and extract insights Social network analysis algorithms like PageRank and community detection How to translate graph structure to a ML model input with node embedding models Using graph features in node classification and link prediction workflows Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis. It’s filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You’ll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. You don’t need any graph experience to start benefiting from this insightful guide. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects. about the technology Graphs reveal the relationships in your data. Tracking these interlinking connections reveals new insights and influences and lets you analyze each data point as part of a larger whole. This interconnected data is perfect for machine learning, as well as analyzing social networks, communities, and even product recommendations. about the book Graph Algorithms for Data Science teaches you how to construct graphs from both structured and unstructured data. You’ll learn how the flexible Cypher query language can be used to easily manipulate graph structures, and extract amazing insights. The book explores common and useful graph algorithms like PageRank and community detection/clustering algorithms. Each new algorithm you learn is instantly put into action to complete a hands-on data project, including modeling a social network! Finally, you’ll learn how to utilize graphs to upgrade your machine learning, including utilizing node embedding models and graph neural networks. about the reader For data scientists who know the basics of Machine Learning. Examples use the Cypher query language, which is explained in the book. about the author Tomaz Bratanic is a network scientist at heart, working at the intersection of graphs and machine learning. He has applied these graph techniques to projects in various domains including fraud detection, biomedicine, business-oriented analytics, and recommendations. Welcome 1_Graphs_and_network_science:_An_introduction 2_Representing_network_structure_-_design_your_first_graph_model 3_Your_first_steps_with_the_Cypher_query_language 4_Exploratory_graph_analysis 5_Introduction_to_social_network_analysis 6_Projecting_monopartite_networks_with_Cypher_Projection 7_Inferring_co-occurrence_networks_based_off_bipartite_networks 8_Constructing_a_nearest_neighbor_similarity_network 9_Node_embeddings_and_classification 10_Link_prediction 11_Knowledge_graph_completion 12_Construct_a_graph_using_NLP_techniques