This note covers the following topics:
Basic theory about graphs: Connectivity, Paths, Trees, Networks and flows,
Eulerian and Hamiltonian graphs, Coloring problems and Complexity issues, A
number of applications, Large scale problems in graphs, Similarity of nodes in
large graphs, Telephony problems and graphs, Ranking in large graphs, Clustering
of large graphs.
This note explains introduction to graphs,
The very basics, Spanning trees, Extremal graph theory, Matchings, covers
and factor, Flows on networks, vertex and edge connectivity, Chromatic
number and polynomials, Graphs and matrices and planar graphs.
Author(s): D Yogeshwaran Indian
Statistical Institute, Bangalore
This note covers
basics, Proofs, Constructions, Algorithms and applications, Bipartite graphs
and trees, Eulerian and Hamiltonian graphs, Coloring, Planar graphs, Digraphs
and connectivity.
This note covers
preface and introduction to graph theory, Some definitions and theorems, More
definitions and theorems, Some algebraic graph theory, Applications of
algebraic graph theory, Trees, Algorithms and matroids, A brief introduction
to linear programming, An introduction to network flows and combinatorial
optimization, A short introduction to random graphs, Coloring, Some more
algebraic graph theory.
This
PDF book covers the following topics related to Graph Theory :Preliminaries,
Matchings, Connectivity, Planar graphs, Colorings, Extremal graph theory, Ramsey
theory, Flows, Random graphs, Hamiltonian cycles.
This note
describes the following topics: Book-Embeddings and Pagenumber,
Book-Embeddings of Planar Graphs, Extremal Graph Theory, Pagenumber and
Extremal Results, Maximal Book-Embeddings.
This note covers the
following topics: Immersion and embedding of 2-regular digraphs, Flows in
bidirected graphs, Average degree of graph powers, Classical graph properties
and graph parameters and their definability in SOL, Algebraic and
model-theoretic methods in constraint satisfaction, Coloring random and planted
graphs: thresholds, structure of solutions and algorithmic hardness.