## Structural Analysis of Complex NetworksBecause of the increasing complexity and growth of real-world networks, their analysis by using classical graph-theoretic methods is oftentimes a difficult procedure. As a result, there is a strong need to combine graph-theoretic methods with mathematical techniques from other scientific disciplines, such as machine learning and information theory, in order to analyze complex networks more adequately. Filling a gap in literature, this self-contained book presents theoretical and application-oriented results to structurally explore 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. Special emphasis is given to methods related to the following areas: * Applications to biology, chemistry, linguistics, and data analysis * Graph colorings * Graph polynomials * Information measures for graphs * Metrical properties of graphs * Partitions and decompositions * Quantitative graph measures Structural Analysis of Complex Networks 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. The book may be used as a supplementary textbook in graduate-level seminars on structural graph analysis, complex networks, or network-based machine learning methods. |

### What people are saying - Write a review

### Contents

1 | |

Chapter 2 Partitions of Graphs | 27 |

Chapter 3 Distance in Graphs | 49 |

Chapter 4 Domination in Graphs | 73 |

Chapter 5 Spectrum and Entropy for Infinite Directed Graphs | 105 |

Chapter 6 Application of Infinite Labeled Graphs to Symbolic Dynamical Systems | 137 |

Chapter 7 Decompositions and Factorizations of Complete Graphs | 169 |

Chapter 8 Geodetic Sets in Graphs | 197 |

Chapter 12 Subgraphs as a Measure of Similarity | 319 |

Chapter 13 A Chromatic Metric on Graphs | 335 |

Chapter 14 Some Applications of Eigenvalues of Graphs | 357 |

Chapter 15 Minimum Spanning Markovian Trees Introducing ContextSensitivity into the Generation of Spanning Trees | 381 |

Chapter 16 LinkBased Network Mining | 403 |

Chapter 17 Graph Representations and Algorithms in Computational Biology of RNA Secondary Structure | 421 |

Chapter 18 Inference of Protein Function from the Structureof Interaction Networks | 439 |

Chapter 19 Applications of Perfect Matchings in Chemistry | 463 |