Graph machine learning: take graph data to the next level by applying machine learning techniques and algorithms
build machine learning algorithms using graph data and efficiently exploit topological information within your models/bh4Key Features/h4ulliImplement machine learning techniques and algorithms in graph data/liliIdentify the relationship between nodes in order to make better business decisions/liliAp...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Birmingham ; Mumbai
Packt
May 2021
|
| Schlagworte: | |
| Zusammenfassung: | build machine learning algorithms using graph data and efficiently exploit topological information within your models/bh4Key Features/h4ulliImplement machine learning techniques and algorithms in graph data/liliIdentify the relationship between nodes in order to make better business decisions/liliApply graph-based machine learning methods to solve real-life problems/li/ulh4Book Description/h4Graph Machine Learning provides a new set of tools for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks. You will start with a brief introduction to graph theory and graph machine learning, understanding their potential. As you proceed, you will become well versed with 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 then build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. Moving ahead, you will cover 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. Finally, you will learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, before progressing to 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.h4What you will learn/h4ulliWrite Python scripts to extract features from graphs/liliDistinguish between the main graph representation learning techniques/liliBecome well-versed with extracting data from social networks, financial transaction systems, and more/liliImplement the main unsupervised and supervised graph embedding techniques/liliGet to grips with shallow embedding methods, graph neural networks, graph regularization methods, and more/liliDeploy and scale out your application seamlessly/li/ulh4Who this book is for/h4This book is for data scientists, data analysts, graph analysts, and graph professionals who want to leverage the information embedded in the connections and relations between data points to boost their analysis and model performance using machine learning. |
| Beschreibung: | xi, 319 Seiten Illustrationen, Diagramme |
| ISBN: | 9781800204492 |
Internformat
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| 520 | 3 | |a build machine learning algorithms using graph data and efficiently exploit topological information within your models/bh4Key Features/h4ulliImplement machine learning techniques and algorithms in graph data/liliIdentify the relationship between nodes in order to make better business decisions/liliApply graph-based machine learning methods to solve real-life problems/li/ulh4Book Description/h4Graph Machine Learning provides a new set of tools for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks. You will start with a brief introduction to graph theory and graph machine learning, understanding their potential. As you proceed, you will become well versed with 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. | |
| 520 | 3 | |a You'll then build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. Moving ahead, you will cover 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. Finally, you will learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, before progressing to explore the latest trends on graphs. | |
| 520 | 3 | |a 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.h4What you will learn/h4ulliWrite Python scripts to extract features from graphs/liliDistinguish between the main graph representation learning techniques/liliBecome well-versed with extracting data from social networks, financial transaction systems, and more/liliImplement the main unsupervised and supervised graph embedding techniques/liliGet to grips with shallow embedding methods, graph neural networks, graph regularization methods, and more/liliDeploy and scale out your application seamlessly/li/ulh4Who this book is for/h4This book is for data scientists, data analysts, graph analysts, and graph professionals who want to leverage the information embedded in the connections and relations between data points to boost their analysis and model performance using machine learning. | |
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Datensatz im Suchindex
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| author | Stamile, Claudio Marzullo, Aldo Deusebio, Enrico |
| author_GND | (DE-588)123907140X (DE-588)1239128304 (DE-588)1239072333 |
| author_facet | Stamile, Claudio Marzullo, Aldo Deusebio, Enrico |
| author_role | aut aut aut |
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| bvnumber | BV047682278 |
| classification_rvk | ST 300 |
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| discipline | Informatik |
| discipline_str_mv | Informatik |
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| id | DE-604.BV047682278 |
| illustrated | Illustrated |
| index_date | 2024-07-03T18:55:59Z |
| indexdate | 2024-07-10T09:19:06Z |
| institution | BVB |
| isbn | 9781800204492 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033066346 |
| oclc_num | 1264134201 |
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| owner | DE-20 DE-355 DE-BY-UBR |
| owner_facet | DE-20 DE-355 DE-BY-UBR |
| physical | xi, 319 Seiten Illustrationen, Diagramme |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Packt |
| record_format | marc |
| spelling | Stamile, Claudio Verfasser (DE-588)123907140X aut Graph machine learning take graph data to the next level by applying machine learning techniques and algorithms Claudio Stamile, Aldo Marzullo, Enrico Deusebio Birmingham ; Mumbai Packt May 2021 xi, 319 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier build machine learning algorithms using graph data and efficiently exploit topological information within your models/bh4Key Features/h4ulliImplement machine learning techniques and algorithms in graph data/liliIdentify the relationship between nodes in order to make better business decisions/liliApply graph-based machine learning methods to solve real-life problems/li/ulh4Book Description/h4Graph Machine Learning provides a new set of tools for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks. You will start with a brief introduction to graph theory and graph machine learning, understanding their potential. As you proceed, you will become well versed with 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 then build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. Moving ahead, you will cover 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. Finally, you will learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, before progressing to 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.h4What you will learn/h4ulliWrite Python scripts to extract features from graphs/liliDistinguish between the main graph representation learning techniques/liliBecome well-versed with extracting data from social networks, financial transaction systems, and more/liliImplement the main unsupervised and supervised graph embedding techniques/liliGet to grips with shallow embedding methods, graph neural networks, graph regularization methods, and more/liliDeploy and scale out your application seamlessly/li/ulh4Who this book is for/h4This book is for data scientists, data analysts, graph analysts, and graph professionals who want to leverage the information embedded in the connections and relations between data points to boost their analysis and model performance using machine learning. Graphentheorie (DE-588)4113782-6 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf COMPUTERS / Data Processing Maschinelles Lernen (DE-588)4193754-5 s Graphentheorie (DE-588)4113782-6 s DE-604 Marzullo, Aldo Verfasser (DE-588)1239128304 aut Deusebio, Enrico Verfasser (DE-588)1239072333 aut |
| spellingShingle | Stamile, Claudio Marzullo, Aldo Deusebio, Enrico Graph machine learning take graph data to the next level by applying machine learning techniques and algorithms Graphentheorie (DE-588)4113782-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4113782-6 (DE-588)4193754-5 |
| title | Graph machine learning take graph data to the next level by applying machine learning techniques and algorithms |
| title_auth | Graph machine learning take graph data to the next level by applying machine learning techniques and algorithms |
| title_exact_search | Graph machine learning take graph data to the next level by applying machine learning techniques and algorithms |
| title_exact_search_txtP | Graph machine learning take graph data to the next level by applying machine learning techniques and algorithms |
| title_full | Graph machine learning take graph data to the next level by applying machine learning techniques and algorithms Claudio Stamile, Aldo Marzullo, Enrico Deusebio |
| title_fullStr | Graph machine learning take graph data to the next level by applying machine learning techniques and algorithms Claudio Stamile, Aldo Marzullo, Enrico Deusebio |
| title_full_unstemmed | Graph machine learning take graph data to the next level by applying machine learning techniques and algorithms Claudio Stamile, Aldo Marzullo, Enrico Deusebio |
| title_short | Graph machine learning |
| title_sort | graph machine learning take graph data to the next level by applying machine learning techniques and algorithms |
| title_sub | take graph data to the next level by applying machine learning techniques and algorithms |
| topic | Graphentheorie (DE-588)4113782-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Graphentheorie Maschinelles Lernen |
| work_keys_str_mv | AT stamileclaudio graphmachinelearningtakegraphdatatothenextlevelbyapplyingmachinelearningtechniquesandalgorithms AT marzulloaldo graphmachinelearningtakegraphdatatothenextlevelbyapplyingmachinelearningtechniquesandalgorithms AT deusebioenrico graphmachinelearningtakegraphdatatothenextlevelbyapplyingmachinelearningtechniquesandalgorithms |