What is graph machine learning?

Graph machine learning refers to a set of techniques that utilize graph structures to enhance learning algorithms. These graphs represent data as nodes (entities) and edges (relationships), allowing for a more nuanced analysis of data relationships. This approach is especially effective in fields like social networks, recommendation systems, and bioinformatics, where relationships between data points are critical. For instance, a recommendation system can analyze user connections to suggest products or services based on a user's network.

Why is it important to learn about the latest advancements in graph data?

Staying updated with the latest advancements in graph data is crucial for leveraging complex relationships in data analysis. With the continual evolution of machine learning methods, breakthroughs in graph-based algorithms can enhance predictive accuracy and model robustness. These advancements help data scientists and machine learning professionals uncover hidden patterns and insights in various applications, from fraud detection to optimizing supply chains, enabling more informed decision-making.

What are some common use cases for graph machine learning?

Graph machine learning has a variety of practical applications. It's widely used in social network analysis to understand user behaviors and connections, enhancing personalization in recommendations. Additionally, it's employed in fraud detection to track irregular patterns, in bioinformatics for studying protein interactions, and in knowledge graphs to enhance search engines. Each application highlights how understanding relationships in data can yield actionable insights and improve system efficiencies.

What foundational concepts should I understand before diving into graph machine learning?

Before exploring graph machine learning, it's beneficial to grasp key concepts such as graph theory, including nodes, edges, and various types of graphs (directed, undirected, weighted, etc.). Understanding fundamental machine learning principles, like supervised and unsupervised learning, will also enhance your application of these techniques. These foundations will allow you to approach graph models with a clear understanding and apply them effectively in real-world scenarios.

How does graph machine learning differ from traditional machine learning?

Graph machine learning differs from traditional machine learning primarily by its focus on relational data structures. While traditional models typically analyze data as independent features, graph models leverage connections and relationships between data points, which can unveil deeper insights. This is especially beneficial in situations where data points are interdependent, such as in social networks or knowledge systems, leading to improved performance in tasks like node classification and link prediction.

What tools and frameworks are used for graph machine learning?

There are several popular tools and frameworks designed for graph machine learning, including NetworkX, PyTorch Geometric, and DGL (Deep Graph Library). These tools facilitate the manipulation of graph structures and offer built-in algorithms for various graph-related tasks. Depending on your project requirements, you might choose one over the others; for example, PyTorch Geometric integrates well with deep learning frameworks, making it ideal for complex tasks requiring neural networks.

Can you give examples of graph algorithms commonly used in machine learning?

Common graph algorithms used in machine learning include Graph Convolutional Networks (GCNs), which are popular for node classification tasks, and Graph Attention Networks (GATs), which prioritize significant nodes in feature aggregation. Additionally, algorithms like PageRank and clustering techniques help identify influential nodes and group similar items, respectively. These algorithms enable you to analyze and predict relationships effectively, enhancing the overall performance of your models.

How can graph machine learning improve recommendations in e-commerce?

Graph machine learning can significantly enhance recommendations in e-commerce by analyzing user behavior and product relationships. By modeling users and products as nodes in a graph, the system can identify patterns, such as which products are often purchased together or which customers have similar purchasing habits. This interconnected understanding allows for more personalized and relevant recommendations, thereby increasing customer satisfaction and driving sales.

What should I consider when selecting datasets for graph machine learning?

When selecting datasets for graph machine learning, consider the size, quality, and structure of the data. It’s critical to ensure that the dataset represents the relationships accurately and contains nodes and edges in a manner that can be effectively analyzed. Moreover, look for datasets relevant to your specific problem domain, as this relevance will help you achieve more accurate and meaningful results when training your models.

Come acquistare Graph Machine Learning: Learn about the latest online da Ubuy?

È facile acquistare Graph Machine Learning: Learn about the latest online da Ubuy.. Devi solo cercare il prodotto, scegliere il metodo di spedizione durante il check-out e farlo consegnare alla tua posizione.

Graph Machine Learning: Learn about the latest è disponibile per gli acquisti online in Italy?

Sì, su Ubuy Italy questo prodotto è disponibile per gli acquisti a un prezzo ragionevole.. Il Graph Machine Learning: Learn about the latest non è disponibile localmente ma puoi fidarti di noi con i nostri servizi di spedizione espressa.

Quanto tempo ci vuole per ottenere il prodotto dopo aver effettuato l'ordine?

I tempi di consegna del prodotto ordinato variano in base a ciò che hai ordinato e al metodo di spedizione che hai scelto.. Il tempo di consegna stimato viene menzionato durante il processo di pagamento, quindi sii spensierato durante lo shopping.

Visualizza originale

Graph Machine Learning: Learn about the latest advancements in graph data to build robust machine learning models

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.

Articolo #: 167344667

Graph Machine Learning: Learn about the latest advancements in graph data to build robust machine learning models

Q: Where can I buy Graph Machine Learning: Learn About The Latest Advancements In Graph Data To Build Robust Machine Learning Models in Italy?

You can purchase 'Graph Machine Learning: Learn About The Latest Advancements In Graph Data To Build Robust Machine Learning Models' on Ubuy. This platform offers a wide range of products, including books on cutting-edge technologies like graph machine learning, providing users access to the latest resources in the field.

90% degli intervistati lo consiglierebbe a un amico

Articolo #: 167344667

€ 48

Dettagli del prezzo

Escluse spese di spedizione e doganali ( Le spese di spedizione e doganali saranno calcolate al momento del pagamento )

^{*Tutti gli articoli verranno importati da Stati Uniti}

Disponibilità : Disponibile

Importato dal negozio USA

Qtà:

Ordina subito e ricevilo entro Mercoledì, Ottobre 21

Transazione sicura

I nostri principali partner logistici

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.

Paperback

Garanzia U-Care:

Nessuna

Seleziona un piano

Compra ora, paga dopo

Consegna
veloce

Reso
gratuito^*

Imballaggio sicuro

Prodotti originali al 100%

Conformità PCI DSS

Certificazione ISO 27001

Note: Step Down Voltage Transformer required for using electronics products of Stati Uniti store (110-120). Recommended power converters Acquista ora.

Cosa salta all'occhio

Advanced Graph Techniques

Stay updated with the latest methodologies and algorithms in graph machine learning, enabling more robust model training and improved data insights for various applications.

Comprehensive Learning

Gain in-depth knowledge of both theory and practical applications, ensuring a strong foundation for building effective models on graph data.

Industry Relevance

Explore real-world case studies that demonstrate the effectiveness of graph machine learning, equipping learners with applicable skills for today’s competitive data science landscape.

Dettagli del prodotto

Enhance your data science skills with this updated edition featuring new chapters on LLMs, temporal graphs, and updated examples with modern frameworks, including PyTorch Geometric and DGLFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesMaster new graph ML techniques through updated examples using PyTorch Geometric and Deep Graph Library (DGL)Explore GML frameworks and their main characteristicsLeverage LLMs for machine learning on graphs and learn about temporal learningPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGraph 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 -What you will learnImplement graph ML algorithms with examples in StellarGraph, PyTorch Geometric, and DGLApply graph analysis to dynamic datasets using temporal graph MLEnhance NLP and text analytics with graph-based techniquesSolve complex real-world problems with graph machine learningBuild and scale graph-powered ML applications effectivelyDeploy and scale your application seamlesslyWho this book is forThis book is for data scientists, ML professionals, and graph specialists looking to deepen their knowledge of graph data analysis or expand their machine learning toolkit. Prior knowledge of Python and basic machine learning principles is recommended.Table of ContentsGetting Started with GraphsGraph Machine LearningNeural Networks and GraphsUnsupervised Graph LearningSupervised Graph LearningSolving Common Graph-Based Machine Learning ProblemsSocial Network GraphsText Analytics and Natural Language Processing Using GraphsGraph Analysis for Credit Card TransactionsBuilding a Data-Driven Graph-Powered ApplicationTemporal Graph Machine Learning GraphML and LLMsNovel Trends on Graphs

Publisher	Packt Publishing
Publication date	July 18, 2025
Edition	2nd ed.
Language	English
Print length	434 pages
ISBN-10	1803248068
ISBN-13	978-1803248066
Item Weight	1.63 pounds (740 grams)
Dimensions	7.5 x 0.98 x 9.25 inches (19.1 x 2.5 x 23.5 cm)

A chi è consigliato?

Data Scientists
Data scientists seeking to enhance their machine learning models by incorporating graph-based techniques and advanced data structures.
AI Researchers
AI researchers focused on exploring cutting-edge advancements in graph machine learning for innovative projects and studies.
Students and Learners
Students aiming to learn about graph data applications in machine learning for academic or practical purposes, enhancing their skills.

Beginners in AI
Complete beginners in AI may struggle with the advanced concepts of graph machine learning without prior knowledge.

DESCRIZIONE DEL PRODOTTO

Hai qualche domanda? chatta con noi

Domande e risposte dei clienti

Domanda: Come acquistare Graph Machine Learning: Learn about the latest online da Ubuy?

Risposta: È facile acquistare Graph Machine Learning: Learn about the latest online da Ubuy.. Devi solo cercare il prodotto, scegliere il metodo di spedizione durante il check-out e farlo consegnare alla tua posizione.
Domanda: Graph Machine Learning: Learn about the latest è disponibile per gli acquisti online in Italy?

Risposta: Sì, su Ubuy Italy questo prodotto è disponibile per gli acquisti a un prezzo ragionevole.. Il Graph Machine Learning: Learn about the latest non è disponibile localmente ma puoi fidarti di noi con i nostri servizi di spedizione espressa.
Domanda: Quanto tempo ci vuole per ottenere il prodotto dopo aver effettuato l'ordine?

Risposta: I tempi di consegna del prodotto ordinato variano in base a ciò che hai ordinato e al metodo di spedizione che hai scelto.. Il tempo di consegna stimato viene menzionato durante il processo di pagamento, quindi sii spensierato durante lo shopping.

Neural Networks Editorial Review

Graph Machine Learning: Learn about the latest advancements in graph data to build robust machine learning models offers an insightful journey into the dynamic world of graph-based algorithms. The authors seamlessly blend theory with practical applications, providing readers with a deep understanding of how Graph Neural Networks (GNNs) function in real-world scenarios like product recommendations and fraud detection. Readers particularly appreciate the updated content that covers modern techniques, including LLM integration and temporal graphs, along with hands-on examples using popular libraries. This book makes it clear that graphs are fundamental to capturing the relationships that drive our reality and are poised to revolutionize various industries.

Recensioni e valutazioni dei clienti

4.8

21 valutazioni dei clienti

5 stella

84%
4 stella

16%
3 stella

0%
2 stella

0%
1 stella

0%

Recensisci questo prodotto

Condividi le tue impressioni con altri clienti

Vantaggi

Practical applications for real-world scenarios
Covers modern techniques effectively
Clear explanations of complex concepts
Hands-on examples with popular libraries
Insightful architecture of relationships

Contro

May be advanced for beginners in ML

Cronologia dei prezzi del prodotto

Informazioni importanti

Limitazioni: per i prodotti spediti a livello internazionale, si prega di notare che ogni garanzia del produttore potrebbe non essere valida, le opzioni di assistenza del produttore potrebbero non essere disponibili, i manuali del prodotto, le istruzioni e gli avvisi di sicurezza potrebbero non essere nella lingua del paese di destinazione. I prodotti (e i materiali di accompagnamento) potrebbero non essere progettati in conformità con gli standard, le specifiche e i requisiti di etichettatura del paese di destinazione e i prodotti potrebbero non essere conformi alla tensione di rete del paese di destinazione e ad altri standard elettrici (richiedendo l'uso di un adattatore o di un trasformatore, se necessario). Il destinatario è responsabile di assicurare che il prodotto possa essere legalmente importato nel paese di destinazione. Quando si ordina da Ubuy o dai suoi affiliati, il destinatario è l'importatore registrato ed è tenuto a rispettare tutte le leggi e i regolamenti del paese di destinazione.
Non tutti i prodotti elencati su Ubuy sono in vendita, poiché Ubuy è un motore di ricerca globale. I prodotti sono soggetti a regolamenti di esportazione/commercio.

€ 48

Ordina subito e ricevilo entro Mercoledì, Ottobre 21

Qtà:

Transazione sicura

Conforme allo standard PCI DSS e certificato ISO 27001:2022, con pagamenti crittografati e piena protezione dell'acquirente su ogni ordine.

I nostri principali partner logistici

Consegna transfrontaliera più rapida

Caratteristiche e benefici

Master cutting-edge graph ML techniques with real-world examples.
Explore modern frameworks like PyTorch Geometric and DGL.
Learn to leverage LLMs and temporal graphs for advanced insights.
Gain essential problem-solving skills for evolving technologies.
Build and scale graph-powered applications efficiently.
Ideal for data scientists, ML professionals, and graph specialists.

Garanzia Ubuy

Vivi un'esperienza di shopping senza pensieri con prodotti 100% originali, sicurezza dei pagamenti conforme allo standard PCI DSS, protezione dei dati certificata ISO 27001, la consegna internazionale più rapida, resi gratuiti, e imballaggio sicuro su ogni ordine.*

Graph Machine Learning: Learn about the latest advancements in graph data to build robust machine learning models