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Modern graph theory

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TLDR
This book presents an account of newer topics, including Szemer'edi's Regularity Lemma and its use; Shelah's extension of the Hales-Jewett Theorem; the precise nature of the phase transition in a random graph process; the connection between electrical networks and random walks on graphs; and the Tutte polynomial and its cousins in knot theory.
Abstract
The time has now come when graph theory should be part of the education of every serious student of mathematics and computer science, both for its own sake and to enhance the appreciation of mathematics as a whole. This book is an in-depth account of graph theory, written with such a student in mind; it reflects the current state of the subject and emphasizes connections with other branches of pure mathematics. The volume grew out of the author's earlier book, Graph Theory -- An Introductory Course, but its length is well over twice that of its predecessor, allowing it to reveal many exciting new developments in the subject. Recognizing that graph theory is one of several courses competing for the attention of a student, the book contains extensive descriptive passages designed to convey the flavor of the subject and to arouse interest. In addition to a modern treatment of the classical areas of graph theory such as coloring, matching, extremal theory, and algebraic graph theory, the book presents a detailed account of newer topics, including Szemer\'edi's Regularity Lemma and its use, Shelah's extension of the Hales-Jewett Theorem, the precise nature of the phase transition in a random graph process, the connection between electrical networks and random walks on graphs, and the Tutte polynomial and its cousins in knot theory. In no other branch of mathematics is it as vital to tackle and solve challenging exercises in order to master the subject. To this end, the book contains an unusually large number of well thought-out exercises: over 600 in total. Although some are straightforward, most of them are substantial, and others will stretch even the most able reader.

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Proceedings ArticleDOI

Average consensus over small world networks: A probabilistic framework

TL;DR: It is observed that adding a few long range edges to certain graph topologies can significantly increase the rate of convergence for consensus algorithms, and a probabilistic framework for analyzing this effect is provided.
Posted Content

Distributed Consensus Algorithms in Sensor Networks: Quantized Data and Random Link Failures

TL;DR: The paper shows how to use probability bounds on the excursions of the two subsequences to design the quantizer parameters and to explore tradeoffs among the number of quantizer levels, the size of the quantization steps, the desired probability of saturation, and the desired level of accuracy away from consensus.
Journal ArticleDOI

Structure-based graph distance measures of high degree of precision

TL;DR: This work defines substructure abundance vector (SAV) to capture more substructure information of a graph and proposes unified graphdistance measures which are generalization of the existing structure-based graph distance measures.
Journal ArticleDOI

Canonical horizontal visibility graphs are uniquely determined by their degree sequence

TL;DR: In this paper, it is shown that under suitable conditions, there exists a bijection between the adjacency matrix of an HVG and its degree sequence, and an explicit construction of such bijection is given.
Journal ArticleDOI

On the location of roots of graph polynomials

TL;DR: In this paper, the authors examine to what extent the location of these roots reflects the graph theoretic properties of the underlying graph, and show that the locations of the roots reflect the properties of a graph.