TY - BOOK ID - 8435278 TI - Structural analysis of complex networks : theory and applications PY - 2011 SN - 0817647880 9786612973642 1282973649 0817647899 PB - Dordrecht : Birkhauser, DB - UniCat KW - Graph theory. KW - Network theory. KW - System analysis. KW - System analysis KW - Graph theory KW - Mathematics KW - Civil & Environmental Engineering KW - Engineering & Applied Sciences KW - Physical Sciences & Mathematics KW - Applied Mathematics KW - Operations Research KW - Algebra KW - Computer networks. KW - Artificial intelligence. KW - Machine learning. KW - Computational linguistics. KW - Automatic language processing KW - Language and languages KW - Language data processing KW - Linguistics KW - Natural language processing (Linguistics) KW - Learning, Machine KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Network theory KW - Systems analysis KW - Communication systems, Computer KW - Computer communication systems KW - Data networks, Computer KW - ECNs (Electronic communication networks) KW - Electronic communication networks KW - Networks, Computer KW - Teleprocessing networks KW - Data processing KW - Mathematics. KW - Computer communication systems. KW - Computer science KW - Data mining. KW - Bioinformatics. KW - Applied mathematics. KW - Engineering mathematics. KW - Combinatorics. KW - Applications of Mathematics. KW - Discrete Mathematics in Computer Science. KW - Computer Communication Networks. KW - Computational Biology/Bioinformatics. KW - Data Mining and Knowledge Discovery. KW - Applied linguistics KW - Cross-language information retrieval KW - Mathematical linguistics KW - Multilingual computing KW - Artificial intelligence KW - Machine theory KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Network analysis KW - Network science KW - System theory KW - Mathematical optimization KW - Data transmission systems KW - Digital communications KW - Electronic systems KW - Information networks KW - Telecommunication KW - Cyberinfrastructure KW - Network computers KW - Distributed processing KW - Computational complexity. KW - Algorithmic knowledge discovery KW - Factual data analysis KW - KDD (Information retrieval) KW - Knowledge discovery in data KW - Knowledge discovery in databases KW - Mining, Data KW - Database searching KW - Bio-informatics KW - Biological informatics KW - Biology KW - Information science KW - Computational biology KW - Systems biology KW - Combinatorics KW - Mathematical analysis KW - Complexity, Computational KW - Math KW - Science KW - Computer science—Mathematics. KW - Engineering KW - Engineering analysis UR - https://www.unicat.be/uniCat?func=search&query=sysid:8435278 AB - Because 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. ER -