Computer Methods For The Symbolic Analysis Of Networks

This book presents novel symbolic control and data flow techniques as well as symbolic techniques and algorithms for program analysis and program optimization. Program contexts, defining a new symbolic description of program semantics for control and data flow analysis, are at the center of the techniques and methods introduced. The authors develop solutions for a number of problems encountered in program analysis by using program contexts. The solutions proposed are efficient, versatile, unified, and more general than most existing methods. The authors' symbolic analysis framework is implemented as a prototype as part of the Vienna High Performance Compiler.

Data science unifies statistics, data analysis and machine learning to achieve a better understanding of the masses of data which are produced today, and to improve prediction. Special kinds of data (symbolic, network, complex, compositional) are increasingly frequent in data science. These data require specific methodologies, but there is a lack of reference work in this field. Advances in Data Science fills this gap. It presents a collection of up-to-date contributions by eminent scholars following two international workshops held in Beijing and Paris. The 10 chapters are organized into four parts: Symbolic Data, Complex Data, Network Data and Clustering. They include fundamental contributions, as well as applications to several domains, including business and the social sciences.

This book presents a perspective of network analysis as a tool to find and quantify significant structures in the interaction patterns between different types of entities. Moreover, network analysis provides the basic means to relate these structures to properties of the entities. It has proven itself to be useful for the analysis of biological and social networks, but also for networks describing complex systems in economy, psychology, geography, and various other fields. Today, network analysis packages in the open-source platform R and other open-source software projects enable scientists from all fields to quickly apply network analytic methods to their data sets. Altogether, these applications offer such a wealth of network analytic methods that it can be overwhelming for someone just entering this field. This book provides a road map through this jungle of network analytic methods, offers advice on how to pick the best method for a given network analytic project, and how to avoid common pitfalls. It introduces the methods which are most often used to analyze complex networks, e.g., different global network measures, types of random graph models, centrality indices, and networks motifs. In addition to introducing these methods, the central focus is on network analysis literacy – the competence to decide when to use which of these methods for which type of question. Furthermore, the book intends to increase the reader's competence to read original literature on network analysis by providing a glossary and intensive translation of formal notation and mathematical symbols in everyday speech. Different aspects of network analysis literacy – understanding formal definitions, programming tasks, or the analysis of structural measures and their interpretation – are deepened in various exercises with provided solutions. This text is an excellent, if not the best starting point for all scientists who want to harness the power of network analysis for their field of expertise.

The value of symbolic network analysis is now well recognized. In industry it has been used as an aid in the design of small linear networks. In academic institutions it has been found useful as an instructional aid. The purpose of this book is to present, in a single volume, a unified treatment of all symbolic analysis methods, using a consistent set of notation, and based on the same theoretical background (network topology, combinatorial analysis, and numerical analysis). The emphasis is on those methods which have been implemented and for which there are source codes available. The work will be of interest to all those who have the usual college-level training in circuit theory.

This text is about methods used for the computer simulation of analog systems. It concentrates on electronic applications, but many of the methods are applicable to other engineering problems as well. This revised edition (1st, 1983) encompasses recent theoretical developments and program-writing tips for computer-aided design. About 60% of the text is suitable for a senior-level course in circuit theory. The whole text is suitable for graduate courses or as a reference for scientists and engineers who seek information in the field. Annotation copyright by Book News, Inc., Portland, OR