Bratanic T. Graph Algorithms for Data Science (MEAP v7)

Manning Publications, 2023. — 386 p. — (MEAP v7).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 workflowsGraph 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.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