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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.

Graph theory. --- Network theory. --- System analysis. --- System analysis --- Graph theory --- Mathematics --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Physical Sciences & Mathematics --- Applied Mathematics --- Operations Research --- Algebra --- Computer networks. --- Artificial intelligence. --- Machine learning. --- Computational linguistics. --- Automatic language processing --- Language and languages --- Language data processing --- Linguistics --- Natural language processing (Linguistics) --- Learning, Machine --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Network theory --- Systems analysis --- Communication systems, Computer --- Computer communication systems --- Data networks, Computer --- ECNs (Electronic communication networks) --- Electronic communication networks --- Networks, Computer --- Teleprocessing networks --- Data processing --- Mathematics. --- Computer communication systems. --- Computer science --- Data mining. --- Bioinformatics. --- Applied mathematics. --- Engineering mathematics. --- Combinatorics. --- Applications of Mathematics. --- Discrete Mathematics in Computer Science. --- Computer Communication Networks. --- Computational Biology/Bioinformatics. --- Data Mining and Knowledge Discovery. --- Applied linguistics --- Cross-language information retrieval --- Mathematical linguistics --- Multilingual computing --- Artificial intelligence --- Machine theory --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Network analysis --- Network science --- System theory --- Mathematical optimization --- Data transmission systems --- Digital communications --- Electronic systems --- Information networks --- Telecommunication --- Cyberinfrastructure --- Network computers --- Distributed processing --- Computational complexity. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- Combinatorics --- Mathematical analysis --- Complexity, Computational --- Math --- Science --- Computer science—Mathematics. --- Engineering --- Engineering analysis

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Das Web Mining, welches aus den Teilgebieten Web Structure Mining, Web Content Mining und Web Usage Mining besteht, erlangt im Zuge der Web-basierten Kommunikation eine immer stärkere Bedeutung. Aufgrund unüberschaubarer Datenmengen im Web sind gerade leistungsfähige Verfahren zur Gewinnung und Analyse Web-basierter Informationen von großer Wichtigkeit. Matthias Dehmer rückt das Web Structure Mining, insbesondere die strukturelle Analyse Web-basierter Hypertexte auf Grundlage gerichteter Graphen, in den Mittelpunkt seiner Untersuchung. Der Autor stellt ein graphentheoretisches Modell zur Bestimmung der strukturellen Ähnlichkeit einer Klasse von gerichteten Graphen vor. Auf Basis des angesprochenen Modells führt er Experimente mit bestehenden Hypertexten durch und beschreibt neuartige Anwendungen im Web Structure Mining und in anderen Gebieten.

Data structures (Computer science). --- Information technology. --- Business—Data processing. --- Data Structures. --- IT in Business. --- Data structures (Computer science)

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"Graph-based approaches have been employed extensively in several disciplines such as biology, computer science, chemistry, and so forth. In the 1990s, exploration of the topology of complex networks became quite popular and was triggered by the breakthrough of the Internet and the examinations of random networks. As a consequence, the structure of random networks has been explored using graph-theoretic methods and stochastic growth models. However, it turned out that besides exploring random graphs, quantitative approaches to analyze networks are crucial as well. This relates to quantifying structural information of complex networks by using ameasurement approach. As demonstrated in the scientific literature, graph- and informationtheoretic measures, and statistical techniques applied to networks have been used to do this quantification. It has been found that many real-world networks are composed of network patterns representing nonrandom topologies.Graph- and information-theoretic measures have been proven efficient in quantifying the structural information of such patterns. The study of relevant literature reveals that quantitative graph theory has not yet been considered a branch of graph theory"-- "This book presents methods for analyzing graphs and networks quantitatively. Incorporating interdisciplinary knowledge from graph theory, information theory, measurement theory, and statistical techniques, it covers a wide range of quantitative graph-theoretical concepts and methods, including those pertaining to random graphs. Through its broad coverage, the book fills a gap in the contemporary literature of discrete and applied mathematics, computer science, systems biology, and related disciplines"--

Computer science --- Graph theory --- Combinatorial analysis --- Data processing --- Graph theory - Data processing

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Information Theory and Statistical Learning presents theoretical and practical results about information theoretic methods used in the context of statistical learning. The book will present a comprehensive overview of the large range of different methods that have been developed in a multitude of contexts. Each chapter is written by an expert in the field. The book is intended for an interdisciplinary readership working in machine learning, applied statistics, artificial intelligence, biostatistics, computational biology, bioinformatics, web mining or related disciplines. Advance Praise for Information Theory and Statistical Learning: "A new epoch has arrived for information sciences to integrate various disciplines such as information theory, machine learning, statistical inference, data mining, model selection etc. I am enthusiastic about recommending the present book to researchers and students, because it summarizes most of these new emerging subjects and methods, which are otherwise scattered in many places." -- Shun-ichi Amari, RIKEN Brain Science Institute, Professor-Emeritus at the University of Tokyo

Statistical science --- Mass communications --- Computer science --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- mechatronica --- toegepaste informatica --- informatica --- statistiek --- wiskunde --- KI (kunstmatige intelligentie) --- robots --- communicatietechnologie --- AI (artificiële intelligentie)

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For over a decade, complex networks have steadily grown as an important tool across a broad array of academic disciplines, with applications ranging from physics to social media. A  tightly organized collection of carefully-selected papers on the subject, Towards an Information Theory of Complex Networks: Statistical Methods and Applications presents theoretical and practical results about information-theoretic and statistical models of complex networks in the natural sciences and humanities. The book's major goal is to advocate and promote a combination of graph-theoretic, information-theoretic, and statistical methods as a way to better understand and characterize real-world networks. This volume is the first to present a self-contained, comprehensive overview of information-theoretic models of complex networks with an emphasis on applications. It begins with four chapters developing the most significant formal-theoretical issues of network modeling, but the majority of the book is devoted to combining theoretical results with an empirical analysis of real networks. Specific topics include: chemical graph theory ecosystem interaction dynamics social ontologies language networks software systems This work marks a first step toward establishing advanced statistical information theory as a unified theoretical basis of complex networks for all scientific disciplines. As such, it can serve as a valuable resource for a diverse audience of advanced students and professional scientists. It is primarily intended as a reference for research, but could also be a useful supplemental graduate text in courses related to information science, graph theory, machine learning, and computational biology, among others.

System analysis --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Mathematics --- Physical Sciences & Mathematics --- Operations Research --- Mathematical Theory --- Computer Science --- System analysis. --- Network theory --- Systems analysis --- Mathematics. --- Coding theory. --- Artificial intelligence. --- Applied mathematics. --- Engineering mathematics. --- Information theory. --- Biomathematics. --- Electrical engineering. --- Information and Communication, Circuits. --- Coding and Information Theory. --- Physiological, Cellular and Medical Topics. --- Communications Engineering, Networks. --- Artificial Intelligence (incl. Robotics). --- Applications of Mathematics. --- Network analysis --- Network science --- System theory --- Mathematical optimization --- Physiology --- Telecommunication. --- Artificial Intelligence. --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Electric communication --- Mass communication --- Telecom --- Telecommunication industry --- Telecommunications --- Communication --- Information theory --- Telecommuting --- Animal physiology --- Animals --- Biology --- Anatomy --- Data compression (Telecommunication) --- Digital electronics --- Signal theory (Telecommunication) --- Computer programming --- Math --- Science --- Engineering --- Engineering analysis --- Mathematical analysis --- Electric engineering --- Communication theory --- Cybernetics