Matthias Dehmer

The textbook provides students with tools they need to analyze complex data using methods from data science, machine learning and artificial intelligence. The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data.

The textbook provides students with tools they need to analyze complex data using methods from data science, machine learning and artificial intelligence. The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data. The authors cover all three main components of data science: computer science; mathematics and statistics; and domain knowledge. The book presents methods and implementations in R side-by-side, allowing the immediate practical application of the learning concepts. Furthermore, this teaches computational thinking in a natural way. The book includes exercises, case studies, Q&A and examples.

The aim of the book is to help students become data scientists. Since this requires a series of courses over a considerable period of time, the book intends to accompany students from the beginning to an advanced understanding of the knowledge and skills that define a modern data scientist. The book presents a comprehensive overview of the mathematical foundations of the programming language R and of its applications to data science.

This comprehensive introduction to computational network theory as a branch of network theory builds on the understanding that such networks are a tool to derive or verify hypotheses by applying computational techniques to large scale network data.The highly experienced team of editors and high-profile authors from around the world present and explain a number of methods that are representative of computational network theory, derived from graph theory, as well as computational and statistical techniques. With its coherent structure and homogenous style, this reference is equally suitable for courses on computational networks.

Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internationally renowned researchers in the field of interdisciplinary network theory, the book presents current and classical methods to analyze networks statistically. Methods from machine learning, data mining, and information theory are strongly emphasized throughout. Real data sets are used to showcase the discussed methods and topics, which include: A survey of computational approaches to reconstruct and partition biological networksAn introduction to complex networks—measures, statistical properties, and modelsModeling for evolving biological networksThe structure of an evolving random bipartite graphDensity-based enumeration in structured dataHyponym extraction employing a weighted graph kernel Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduate-level, cross-disciplinary courses in applied discrete mathematics, bioinformatics, pattern recognition, and computer science. The book is also a valuable reference for researchers and practitioners in the fields of applied discrete mathematics, machine learning, data mining, and biostatistics.

Die vorliegende Arbeit entstand im Rahmen meiner T/itigkeit als Doktorand im Fachgebiet Telekooperation des Fachbereichs Informatik an der Technischen U- versit/it Darmstadt. Meinem Doktorvater Prof. Dr. Max Miihlh/iuser danke ich fiir die grofie Fr- heit, mit der ich fachlich das Thema bearbeiten und die Arbeit erstellen ko- te. Dadurch, dass er mir alle MSglichkeiten innerhalb seines Fachgebiets zur Verftigung stellte und mich f6rderte, schaffte er die Voraussetzung ftir eine r- bungslose Durchfiihrung der Arbeit. Diese Unterstiitzung hat mir sehr geholfen. Auch menschlich verdanke ich ihm sehr viel, so dass ohne ihn die Arbeit in der von mir angestrebten Zeit nicht zustande gekommen w/ire. Prof. Dr. Alexander Mehler, der die Zweitgutachtert/itigkeit iibernahm, danke ich einerseits fiir die besonders gute und fruchtbare Zusammenarbeit w/ihrend meiner Dissertationsphase. Unsere Zusammenarbeit im Rahmen von Publikationen und Diskussionen wirkte sich sehr positiv auf die Erstellung der Arbeit aus, so dass er mafigeblich die Qualit/it dieser Arbeit verbesserte. Weiterhin danke ich in diesem Zusammenhang Dipl.-Inform. Rfidiger Gleim, der im Rahmen dieser Arbeit mit grofiem Elan seine Diplomarbeit anfertigte. Damit unterstiitzte er mich stark mit Implementierungsarbeiten und anregenden Diskussionen. Dr. Frank Emmert-Streib danke ich zum einen die/iufierst gute und erfrisch- de Zusammenarbeit und zum anderen wertvolle und konstruktive Hinweise, betreffend Kapitel (6). Dr. Jfirgen Kilian gebiihrt mein Dank die Mithilfe zur Kl/irung grundlegender Konstruktionsmerkmale des Graph/ihnlichkeitsmodells, insbesondere bezfiglich praktischer Aspekte der dynamischen Programmierung.