eBook
Graph Learning Techniques
| Title: | Graph Learning Techniques |
|---|---|
| Description: | This comprehensive guide addresses key challenges at the intersection of data science, graph learning, and privacy preservation. It begins with foundational graph theory, covering essential definitions, concepts, and various types of graphs. The book bridges the gap between theory and application, equipping readers with the skills to translate theoretical knowledge into actionable solutions for complex problems. It includes practical insights into brain network analysis and the dynamics of COVID-19 spread. The guide provides a solid understanding of graphs by exploring different graph representations and the latest advancements in graph learning techniques. It focuses on diverse graph signals and offers a detailed review of state-of-the-art methodologies for analyzing these signals. A major emphasis is placed on privacy preservation, with comprehensive discussions on safeguarding sensitive information within graph structures. The book also looks forward, offering insights into emerging trends, potential challenges, and the evolving landscape of privacy-preserving graph learning. This resource is a valuable reference for advance undergraduate and postgraduate students in courses related to Network Analysis, Privacy and Security in Data Analytics, and Graph Theory and Applications in Healthcare. |
| Authors: | Baoling Shan, Xin Yuan, Wei Ni, Ren Ping Liu, Eryk Dutkiewicz |
| Resource Type: | eBook. |
| Subjects: | Signal processing, Graph theory--Data processing, Graph algorithms |
| Categories: | COMPUTERS / Data Science / Machine Learning, MATHEMATICS / Graphic Methods, MATHEMATICS / Topology |
| Database: | eBook Index |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: edsebk DbLabel: eBook Index An: 4016370 RelevancyScore: 981 AccessLevel: 6 PubType: eBook PubTypeId: ebook PreciseRelevancyScore: 981.043701171875 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Graph Learning Techniques – Name: Abstract Label: Description Group: Ab Data: This comprehensive guide addresses key challenges at the intersection of data science, graph learning, and privacy preservation. It begins with foundational graph theory, covering essential definitions, concepts, and various types of graphs. The book bridges the gap between theory and application, equipping readers with the skills to translate theoretical knowledge into actionable solutions for complex problems. It includes practical insights into brain network analysis and the dynamics of COVID-19 spread. The guide provides a solid understanding of graphs by exploring different graph representations and the latest advancements in graph learning techniques. It focuses on diverse graph signals and offers a detailed review of state-of-the-art methodologies for analyzing these signals. A major emphasis is placed on privacy preservation, with comprehensive discussions on safeguarding sensitive information within graph structures. The book also looks forward, offering insights into emerging trends, potential challenges, and the evolving landscape of privacy-preserving graph learning. This resource is a valuable reference for advance undergraduate and postgraduate students in courses related to Network Analysis, Privacy and Security in Data Analytics, and Graph Theory and Applications in Healthcare. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Baoling+Shan%22">Baoling Shan</searchLink><br /><searchLink fieldCode="AR" term="%22Xin+Yuan%22">Xin Yuan</searchLink><br /><searchLink fieldCode="AR" term="%22Wei+Ni%22">Wei Ni</searchLink><br /><searchLink fieldCode="AR" term="%22Ren+Ping+Liu%22">Ren Ping Liu</searchLink><br /><searchLink fieldCode="AR" term="%22Eryk+Dutkiewicz%22">Eryk Dutkiewicz</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+theory--Data+processing%22">Graph theory--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+algorithms%22">Graph algorithms</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Data+Science+%2F+Machine+Learning%22">COMPUTERS / Data Science / Machine Learning</searchLink><br /><searchLink fieldCode="ZK" term="%22MATHEMATICS+%2F+Graphic+Methods%22">MATHEMATICS / Graphic Methods</searchLink><br /><searchLink fieldCode="ZK" term="%22MATHEMATICS+%2F+Topology%22">MATHEMATICS / Topology</searchLink> |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=4016370 |
| RecordInfo | BibRecord: BibEntity: Classifications: – Code: 511.5 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: Signal processing Type: general – SubjectFull: Graph theory--Data processing Type: general – SubjectFull: Graph algorithms Type: general Titles: – TitleFull: Graph Learning Techniques Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Baoling Shan – PersonEntity: Name: NameFull: Xin Yuan – PersonEntity: Name: NameFull: Wei Ni – PersonEntity: Name: NameFull: Ren Ping Liu – PersonEntity: Name: NameFull: Eryk Dutkiewicz – PersonEntity: Name: NameFull: Baoling Shan – PersonEntity: Name: NameFull: Xin Yuan – PersonEntity: Name: NameFull: Wei Ni – PersonEntity: Name: NameFull: Ren Ping Liu – PersonEntity: Name: NameFull: Eryk Dutkiewicz IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2025 – D: 01 M: 01 Type: profile Y: 2025 Identifiers: – Type: isbn-print Value: 9781032851129 – Type: isbn-print Value: 9781032851136 – Type: isbn-electronic Value: 9781040302217 – Type: isbn-electronic Value: 9781040302231 Titles: – TitleFull: Graph Learning Techniques Type: main |
| ResultId | 1 |