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.