Introduction to graphs

TL;DR

This paper provides a comprehensive pedagogical introduction to graph theory, covering basic concepts, algorithms, and probabilistic analysis of random graphs, serving as foundational material for advanced topics in combinatorial optimization and statistical physics.

Contribution

It offers a structured, accessible overview of graph theory fundamentals, algorithms, and probabilistic tools, forming a basis for studying phase transitions and complex systems.

Findings

Introduction of basic graph theoretical concepts and problems

Explanation of fundamental algorithms for graph analysis

Analysis of random graphs and emergence of giant components

Abstract

Graph theory provides fundamental concepts for many fields of science like statistical physics, network analysis and theoretical computer science. Here we give a pedagogical introduction to graph theory, divided into three sections. In the first, we introduce some basic notations and graph theoretical problems, e.g. Eulerian circuits, vertex covers, and graph colorings. The second section describes some fundamental algorithmic concepts to solve basic graph problems numerically, as, e.g., depth-first search, calculation of strongly connected components, and minimum-spanning tree algorithms. The last section introduces random graphs and probabilistic tools to analyze the emergence of a giant component and a giant q-core in these graphs. The presented text is published as the third chapter of the book "Phase Transitions in Combinatorial Optimization Problem" (Wiley-VCH 2005). Together with introductions to algorithms, to complexity theory and to basic statistical mechanics over random structures, it provides the technical basis for the more advanced chapters. These cover the analysis of phase transitions in combinatorial optimization problems, algorithmic and analytical approaches based on statistical physics tools (replica and cavity methods), the analysis of various search algorithms and the development of efficient heuristic algorithms, based on message passing techniques (warning, belief, and survey propagation).