Showing results for "Distributed Graph Algorithms for Computer Networks Kayhan Erciyes German"

This book presents a comprehensive review of key distributed graph algorithms for computer network applications, with a particular emphasis on practical implementation. Topics and features: introduces a range of fundamental graph algorithms, covering spanning trees, graph traversal algorithms, routing algorithms, and self-stabilization; reviews graph-theoretical distributed approximation algorithms with applications in ad hoc wireless networks; describes in detail the implementation of eac...

Solve challenging and computationally intensive analytics problems by leveraging network science and graph algorithms Key FeaturesLearn how to wrangle different types of datasets and analytics problems into networksLeverage graph theoretic algorithms to analyze data efficientlyApply the skills you gain to solve a variety of problems through case studies in PythonPurchase of the print or Kindle book includes a free PDF eBook

What if the most important information in your data lies not in individual rows and columns, but in the connections between them? Graph machine learning helps uncover patterns hidden in these relationships.Graph Machine Learning Essentials is a practical and accessible guide to understanding how machine learning works with graph-structured data, where entities are connected through relationships.Designed for software engineers, ML engineers, data scientists, resear...

Elements of Deep Learning for Computer Vision gives a thorough understanding of deep learning and provides highly accurate computer vision solutions while using libraries like PyTorch.This book introduces you to Deep Learning and explains all the concepts required to understand the basic working, development, and tuning of a neural network using Pytorch. The book then addresses the field of computer vision using two libraries, including the Python wrapper/version of OpenCV and PIL. After e...

Design robust graph neural networks with PyTorch Geometric by combining graph theory and neural networks with the latest developments and apps Purchase of the print or Kindle book includes a free PDF eBookKey FeaturesImplement -of-the-art graph neural architectures in PythonCreate your own graph datasets from tabular dataBuild powerful traffic forecasting, recommender systems, and anomaly detection applicationsBook Description

Complex Networks: An Algorithmic Perspective supplies the basic theoretical algorithmic and graph theoretic knowledge needed by every researcher and student of complex networks. This book is about specifying, classifying, designing, and implementing mostly sequential and also parallel and distributed algorithms that can be used to analyze the static properties of complex networks. Providing a focused scope which consists of graph theory and algorithms for complex networks, the book identif...

This unique textbook/reference presents unified coverage of bioinformatics topics relating to both biological sequences and biological networks, providing an in-depth analysis of cutting-edge distributed algorithms, as well as of relevant sequential algorithms. In addition to introducing the latest algorithms in this area, more than fifteen new distributed algorithms are also proposed. Topics and features: reviews a range of open challenges in biological sequences and networks; describes i...

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 computer science disciplines.Graph theory is a traditional mathematical discipline that has evolved as a basic tool for modeling and analyzing the complex relationships between different technological landscapes. Graph theory helps explain the semantic and syntactic ...

This clearly structured textbook/reference presents a detailed and comprehensive review of the fundamental principles of sequential graph algorithms, approaches for NP-hard graph problems, approximation algorithms and heuristics for such problems andimplementation of advanced graph structures in machine learning. The work also provides a comparative analysis of sequential, parallel and distributed graph algorithms – including algorithms for big data – and an investigation into the conversi...

This book constitutes revised selected papers from the 5th International Workshop on Distributed Computing for Emerging Smart Networks, DiCES-N 2025, held in Hammamet, Tunisia, during April 26-27, 2025.The 7 full papers, 2 short papers and 1 invited paper included in this book were carefully reviewed and selected from 24 submissions. These papers focus on following topical sections: Artificial intelligence for safety and security; Artificial intelligence for e-health.

This book constitutes the refereed post-conference proceedings of the 25th International Conference on Distributed and Computer and Communication Networks, DCCN 2022, held in Moscow, Russia, in September 26–29, 2022.The 31 revised full papers and 2 revised short papers were carefully reviewed and selected from 130 submissions. The papers cover the following topics: computer and communication networks; analytical modeling of distributed systems; and distributed systems applications....

The study of distributed algorithms provides the needed background in many real-life applications, such as: distributed real-time systems, wireless sensor networks, mobile ad hoc networks and distributed databases.The main goal of Guide to Distributed Algorithms is to provide a detailed study of the design and analysis methods of distributed algorithms and to supply the implementations of most of the presented algorithms in Python language, which is the unique feature of t...