Graphs-GraphNeuralNetworks – Bücher, CDs, LPs und mehr, Seite 3

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Yves van Gennip, Jeremy Budd
A Prolegomenon to Differential Equations and Variational Methods on Graphs

A bird's eye view of theoretical developments and concrete applications that will enable newcomers to get a flavour of key results. Provides an exhaustive, at the time of writing, bibliography which will …

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Ray D. Sameshima
Lectures on Graph Theory

This book introduces foundational topics such as group theory, fields, linear algebra, matrix theory, and graph theory, providing readers with the essential background needed to understand Feynman diagram…

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Volker Diekert, Manfred Kufleitner, Gerhard Rosenberger, Ulrich Hertrampf
Elemente der diskreten Mathematik

De Gruyter Studium

Das Lehrbuch beschäftigt sich mit den Elementen der Diskreten Mathematik und deren algebraischen Grundlagen, die relevante Anwendungen in der Informatik haben. Notwendige Hilfsmittel werden behandelt, um …

lieferbar ab 9.4.2026.

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Keita Broadwater, Namid Stillman
Graph Neural Networks in Action

About the bookIn Graph Neural Networks in Action you will create deep learning models that are perfect for working with interconnected graph data. Start with a comprehensive introduction to graph data's u…

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Júlio Araújo, Mitre C. Dourado, Fábio Protti, Rudini M. Sampaio
Introduction to Graph Convexity

This book focuses on the computational aspects of graph convexity, with a particular emphasis on path convexity within graphs. It provides a thoughtful introduction to this emerging research field, which …

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Graph Theory in Computer Science

This book is a vital resource for anyone looking to understand the essential role of graph theory as the unifying thread that connects and provides innovative solutions across a wide spectrum of modern co…

lieferbar ab 5.5.2026.

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Dhruv Mubayi, Jacques A. Verstraete
Extremal Graph and Hypergraph Theory

Providing a cohesive reference for advanced undergraduates, graduate students and even experienced researchers, this text contains both introductory and advanced material in extremal graph theory, hypergr…

lieferbar ab 28.2.2026.

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Yuzhuo Li, Yunwei Ryan Li
Graph Theory in Power Converters

Understand the theory behind an area of rapid engineering innovationThe field of power electronics has been rapidly transformed in recent decades by astonishing advances in power converter network techniq…

lieferbar ab 28.4.2026.

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Combinatorics, Graph Theory and Computing

This proceedings volume compiles selected, revised papers presented at the 54th Southeastern International Conference on Combinatorics, Graph Theory, and Computing (SEICCGTC 2023), which took place at Flo…

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Eberhard Triesch
Diskrete Mathematik

Dieses Lehrbuch führt auf prägnante und nachvollziehbare Art in die Abzähl- und Existenzprobleme der diskreten Mathematik ein. Es wendet sich an Studierende der Mathematik, der Informatik und benachbarter…

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R. Rama
Topics in Combinatorics and Graph Theory

The book covers all the basics of both the topics. The topics are sequenced in such a manner that there is a flow in understanding the advances. The first and second chapters cover all the basic methods a…

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Reconstruction and Imaging Motion Estimation, and Graphs in Biomedical Image Analysis

This book constitutes the refereed proceedings of the 7th International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2025, and the First Workshop on Reconstruction and Imaging Motion Estimation,…

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Graph Neural Networks: Essentials and Use Cases

Chapter 1 Applications of Graph Neural Networks in Computer Vision: Transforming Perception Through Structure.- Chapter 2 Large-Scale Learnable Graph Convolutional Networks (LGCNs) Graph Neural Networks (…

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Graph Neural Networks for Neurological Disorders

This book represents a unique and comprehensive resource for understanding the intersection of advanced artificial intelligence (AI) and neurology. By focusing on graph neural networks (GNNs), the book ad…

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Monia Lupparelli, Giovanni Maria Marchetti, Claudia Tarantola
Regression Graph Models for Categorical Data

This book consolidates knowledge on regression chain graph models, often referred to as regression graph models, with a particular emphasis on their parameterizations and inference for the analysis of cat…

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Tomaz Bratanic, Oskar Hane
Essential Graphrag

Upgrade your RAG applications with the power of knowledge graphs.Retrieval Augmented Generation (RAG) is a great way to harness the power of generative AI for information not contained in a LLM's training…

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Model Theory, Computer Science, and Graph Polynomials

Part I: Personal Notes.- My writing.- Some personal remarks about Johann A. Makowsky.- The Swiss Connection.- From a Friend and Publisher.- From graph polynomials to the software industry Lessons from Jan…

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Fan Liu, Liqiang Nie
Advancing Recommender Systems with Graph Convolutional Networks

Preface.- 1) Introduction.- 2) Interest-aware Message-Passing Graph Convolutional Network.- 3) Cluster-based Graph Collaborative Filtering.- 4) Semantic Aspect-aware Graph Convolutional Network.- 5) Attri…

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Graph Convolutional Neural Networks for Computer Vision

Revolutionize your machine learning practice with this essential book that provides expert insights into leveraging Graph Convolutional Networks (GCNNs) to overcome the limitations of traditional CNNs.In …

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Harnessing Artificial Intelligence-Enhanced Graph Models for Biological Discovery

Harnessing Artificial Intelligence-Enhanced Graph Models for Biological Discovery: Unveiling Biological Frontiers introduces revolutionary techniques that merge artificial intelligence with graph-based me…

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