Filling a gap in literature, this self-contained book presents
theoretical and application-oriented results that allow for a
structural exploration of complex networks. The work focuses not
only on classical graph-theoretic methods, but also demonstrates
the usefulness of structural graph theory as a tool for solving
interdisciplinary problems. Applications to biology, chemistry,
linguistics, and data analysis are emphasized.
The book is suitable for a broad, interdisciplinary readership
of researchers, practitioners, and graduate students in discrete
mathematics, statistics, computer science, machine learning,
artificial intelligence, computational and systems biology,
cognitive science, computational linguistics, and mathematical
chemistry. It may also be used as a supplementary textbook in
graduate-level seminars on structural graph analysis, complex
networks, or network-based machine learning methods.
General
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