Graphs and Networks, Kingan

A unique blend of graph theory and the modern applications of networks for students and data science professionals

In Graphs and Networks, accomplished mathematician and author Dr. Sandra R. Kingan delivers up to date and topical coverage of graph theory while tying in the various real-world applications of social, telecommunication, and computer networks. It includes topics like Tuttes Wheels Theorem, Seymours Splitter Theorem, and decomposition theorems, and offers access to an accompanying website containing a growing collection of papers that illustrate the practical applications and techniques of graph theory to network science.

Dr. Kingan has extensively classroom tested the text and includes an Instructors Manual available via written request to the publisher. The book offers practical homework problems to reinforce the presented concepts and limits its use of proof-based, mathematically rigorous problems to advanced topical chapters in Part III. Youll discover a distillation of elements common to large graphs and presentation of particular graphs as examples of the underlying mathematics.

Readers will also benefit from the inclusion of:

• A thorough introduction to basic definitions in graph and network theory, as well as isomorphism, graph substructures, and graph operations
• An exploration of graph statistics, tress, degree sequences, Eulerian circuits, Hamiltonian cycles, planar graphs, and colorings
• A practical discussion of matchings and coverings, graph algorithms, network algorithms, random graphs, spectral graph theory, centrality measures, and network flows
• A concise examination of network reliability, extremal graph theory, higher connectivity, excluded minors, and k-sums

Perfect for upper-undergraduate students in graph theory and network science courses or as a self-study text for graduate level students, Graphs and Networks will also earn a place in the libraries of network scientists and graph theorists seeking an insightful reference for the mathematics underlying network science.