Видавець: 24

Graph Data Modeling in Python. A practical guide to curating, analyzing, and modeling data with graphs

Gary Hutson, Matt Jackson

Graphs have become increasingly integral to powering the products and services we use in our daily lives, driving social media, online shopping recommendations, and even fraud detection. With this book, you’ll see how a good graph data model can help enhance efficiency and unlock hidden insights through complex network analysis.Graph Data Modeling in Python will guide you through designing, implementing, and harnessing a variety of graph data models using the popular open source Python libraries NetworkX and igraph. Following practical use cases and examples, you’ll find out how to design optimal graph models capable of supporting a wide range of queries and features. Moreover, you’ll seamlessly transition from traditional relational databases and tabular data to the dynamic world of graph data structures that allow powerful, path-based analyses. As well as learning how to manage a persistent graph database using Neo4j, you’ll also get to grips with adapting your network model to evolving data requirements.By the end of this book, you’ll be able to transform tabular data into powerful graph data models. In essence, you’ll build your knowledge from beginner to advanced-level practitioner in no time.

Graph Data Processing with Cypher. A practical guide to building graph traversal queries using the Cypher syntax on Neo4j

Ravindranatha Anthapu

While it is easy to learn and understand the Cypher declarative language for querying graph databases, it can be very difficult to master it. As graph databases are becoming more mainstream, there is a dearth of content and guidance for developers to leverage database capabilities fully. This book fills the information gap by describing graph traversal patterns in a simple and readable way.This book provides a guided tour of Cypher from understanding the syntax, building a graph data model, and loading the data into graphs to building queries and profiling the queries for best performance. It introduces APOC utilities that can augment Cypher queries to build complex queries. You’ll also be introduced to visualization tools such as Bloom to get the most out of the graph when presenting the results to the end users.After having worked through this book, you’ll have become a seasoned Cypher query developer with a good understanding of the query language and how to use it for the best performance.

Graph Data Science with Neo4j. Learn how to use Neo4j 5 with Graph Data Science library 2.0 and its Python driver for your project

Estelle Scifo

Neo4j, along with its Graph Data Science (GDS) library, is a complete solution to store, query, and analyze graph data. As graph databases are getting more popular among developers, data scientists are likely to face such databases in their career, making it an indispensable skill to work with graph algorithms for extracting context information and improving the overall model prediction performance.Data scientists working with Python will be able to put their knowledge to work with this practical guide to Neo4j and the GDS library that offers step-by-step explanations of essential concepts and practical instructions for implementing data science techniques on graph data using the latest Neo4j version 5 and its associated libraries. You’ll start by querying Neo4j with Cypher and learn how to characterize graph datasets. As you get the hang of running graph algorithms on graph data stored into Neo4j, you’ll understand the new and advanced capabilities of the GDS library that enable you to make predictions and write data science pipelines. Using the newly released GDSL Python driver, you’ll be able to integrate graph algorithms into your ML pipeline.By the end of this book, you’ll be able to take advantage of the relationships in your dataset to improve your current model and make other types of elaborate predictions.

Graph Machine Learning. Learn about the latest advancements in graph data to build robust machine learning models - Second Edition

Aldo Marzullo, Enrico Deusebio, Claudio Stamile

Graph Machine Learning, Second Edition builds on its predecessor’s success, delivering the latest tools and techniques for this rapidly evolving field. From basic graph theory to advanced ML models, you’ll learn how to represent data as graphs to uncover hidden patterns and relationships, with practical implementation emphasized through refreshed code examples. This thoroughly updated edition replaces outdated examples with modern alternatives such as PyTorch and DGL, available on GitHub to support enhanced learning.The book also introduces new chapters on large language models and temporal graph learning, along with deeper insights into modern graph ML frameworks. Rather than serving as a step-by-step tutorial, it focuses on equipping you with fundamental problem-solving approaches that remain valuable even as specific technologies evolve. You will have a clear framework for assessing and selecting the right tools.By the end of this book, you’ll gain both a solid understanding of graph machine learning theory and the skills to apply it to real-world challenges.*Email sign-up and proof of purchase required -

Graph Machine Learning. Take graph data to the next level by applying machine learning techniques and algorithms

Claudio Stamile, Aldo Marzullo, Enrico Deusebio

Graph Machine Learning will introduce you to a set of tools used for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks. The first chapters will introduce you to graph theory and graph machine learning, as well as the scope of their potential use. You’ll then learn all you need to know about 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 build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. After covering the basics, you’ll be taken through 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. You’ll also learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, and 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.

GraphQL Best Practices. Gain hands-on experience with schema design, security, and error handling

Artur Czemiel

In the ever-evolving landscape of web development, GraphQL has emerged as a powerful query language that addresses the limitations of traditional REST APIs. This guide delves deep into GraphQL, starting with its foundational principles and swiftly advancing to complex topics that will equip you with the skills you need to understand this technology.As you progress, you’ll cover advanced concepts such as schema design, security enhancements, and error handling. You'll also explore essential topics such as federation and the transition from REST to GraphQL, gaining a comprehensive understanding of these critical areas. The book helps you learn through practical examples in TypeScript, guiding you through building scalable and secure GraphQL backends. Additionally, you’ll get up to speed with the intricacies of frontend integration.By the end of this book, you’ll have the skills you need to implement real-world GraphQL solutions, creating efficient and robust applications that meet modern web development demands.

GraphQL. Kurs video. Buduj nowoczesne API w Pythonie

Łukasz Przybylski

Obierz kurs na... budowę elastycznych API Application programming interface, czyli słynne API - skrót dobrze znany każdemu programiście. API można zdefiniować jako interfejs programistyczny, który wyznacza sposób komunikowania się aplikacji między sobą. Dotychczas jego struktura była najczęściej określana przez styl architektoniczny REST. W 2015 roku nastąpił przełom: Facebook podzielił się swoim wynalazkiem, a koncept REST zyskał poważnego konkurenta - GraphQL. Ta stosunkowo młoda technologia jest językiem zapytań przeznaczonym do budowania szybkich, elastycznych interfejsów API. Z GraphQL wydajność aplikacji wchodzi w nowy wymiar - otrzymujesz dokładnie to, czego potrzebujesz. Jak to możliwe? Przetwarzane żądania HTTP są agregowane w jeden endpoint, zatem oczekiwane dane, nawet z wielu źródeł, dostajemy w pojedynczym wywołaniu API. W tym kursie video doświadczysz zupełnie innego podejścia do programowania - poznasz alternatywę dla REST. Podążając śladami pionierów Facebooka, razem z GraphQL zoptymalizujesz proces tworzenia i utrzymywania nowoczesnych aplikacji. A więc... zdobądź pożądany na rynku pracy zestaw umiejętności, związanych z obsługą GraphQL i Pythona! Co Cię czeka podczas naszego profesjonalnego szkolenia? Z naszym kursem video nauczysz się: budować nowoczesne API aplikacji webowej tworzyć aplikację z API GraphQL w Pythonie przy użyciu biblioteki Graphene mapować modele z Pythona do GraphQL rozszerzać możliwości modeli w API obsługiwać zapytania do API GraphQL wybierać interesujące dane po stronie klienta modyfikować dane po stronie serwera obsługiwać błędy w GraphQL i bibliotece Graphene tworzyć dokumentację w GraphQL korzystać z zaawansowanych typów, jak interfejsy czy unie testować aplikację Co więcej... dowiesz się, jak zintegrować API GraphQL z bibliotekami Flask i FastAPI wykonasz podstawową integrację modeli Graphene z bazami danych przy użyciu SQLAlchemy i MongoEngine GraphQL. Kurs video. Buduj nowoczesne API w Pythonie ukończysz na poziomie średnio zaawansowanym. W trakcie pierwszych lekcji poznasz niezbędną teorię, typy danych i schemę GraphQL. Następnie zaznajomisz się z rodzajami zapytań, takimi jak Query i Mutation. Zorientujesz się w różnicach między podejściami REST API i GraphQL API i zrozumiesz, jakimi założeniami należy się kierować w doborze architektury oprogramowania. Podczas pisania aplikacji webowej będziesz korzystać z uznanej biblioteki Graphene, przeznaczonej do szybkiego budowania schematów. Krok po kroku nauczysz się mapować modele danych pomiędzy Pythonem a schemą GraphQL i tłumaczyć relacje między nimi. Na koniec dowiesz się, jak integrować GraphQL z mikroframeworkami Flask i FastAPI, a nawet z bazą danych MongoDB. By korzystać z naszego szkolenia, nie musisz być specem od Pythona, jednak podstawowa znajomość tego języka da Ci swobodę i ułatwi pracę z niektórymi modułami kursu. W GraphQL dostajesz to, o co prosisz - naucz się tego używać tak, by działało na Twoją korzyść.

Graveyard Rats

Robert E. Howard

Despite an aversion to the detective formula, he wrote the tales in Graveyard Rats during the same years he chronicled the adventures of Conan. This collection features a new introduction by scholar Don Herron, editor of The Dark Barbarian, the definitive look at the life and work of Robert E. Howard.