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2019 | Buch

Complex Networks in Software, Knowledge, and Social Systems

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Über dieses Buch

This book provides a comprehensive review of complex networks from three different domains, presents novel methods for analyzing them, and highlights applications with accompanying case studies. Special emphasis is placed on three specific kinds of complex networks of high technological and scientific importance: software networks extracted from the source code of computer programs, ontology networks describing semantic web ontologies, and co-authorship networks reflecting collaboration in science. The book is primarily intended for researchers, teachers and students interested in complex networks and network data analysis. However, it will also be valuable for researchers dealing with software engineering, ontology engineering and scientometrics, as it demonstrates how complex network analysis can be used to address important research issues in these three disciplines.

Inhaltsverzeichnis

Frontmatter

Introduction

Frontmatter
Chapter 1. Introduction to Complex Networks
Abstract
Complex networks are graphs describing complex natural, conceptual and engineered systems. In this chapter we present an introduction to complex networks by giving several examples of technological, social, information and biological networks. Then, we discuss complex networks that are in the focus of this monograph (software, ontology and co-authorship networks). Finally, we briefly outline our main research contributions presented in the monograph.
Miloš Savić, Mirjana Ivanović, Lakhmi C. Jain
Chapter 2. Fundamentals of Complex Network Analysis
Abstract
Complex network analysis is a collection of quantitative methods for studying the structure and dynamics of complex networked systems. This chapter presents the fundamentals of complex network analysis. We start out by presenting the basic concepts of complex networks and graph theory. Then, we focus on fundamental network analysis measures and algorithms related to node connectivity, distance, centrality, similarity and clustering. Finally, we discuss fundamental complex network models and their characteristics.
Miloš Savić, Mirjana Ivanović, Lakhmi C. Jain

Software and Ontology Networks: Complex Networks in Source Code

Frontmatter
Chapter 3. Analysis of Software Networks
Abstract
Modern software systems are characterized not only by a large number of constituent software entities (e.g. functions, modules, classes), but also by complex networks of dependencies among those entities. Analysis of software networks can help software engineers and researchers to understand and quantify software design complexity and evaluate software systems according to software design quality principles. In this chapter, we firstly give a comprehensive overview of previous research works dealing with analysis of software networks. Then, we present a novel network-based methodology to analyze software systems. The proposed methodology utilizes the notion of enriched software networks, i.e. software networks whose nodes are augmented with metric vectors containing both software metrics and metrics used in complex network analysis. The methodology is empirically validated on enriched software networks that represent large-scale Java software systems at different levels of abstraction.
Miloš Savić, Mirjana Ivanović, Lakhmi C. Jain
Chapter 4. Analysis of Ontology Networks
Abstract
In computer and information sciences, an ontology is, in its essence, a named set of axioms encoding a network of relationships and dependencies between ontological entities present in a knowledge domain. With the rise of Semantic Web technologies, real-world ontologies have become considerably large leading to complex ontology networks. In this chapter we firstly present an overview of previous research works dealing with analysis of ontology networks. Nodes of ontology networks can be enriched with various metrics reflecting complexity and quality attributes of corresponding ontological entities. On a case study involving one large-scale modularized ontology, we demonstrate that analysis of enriched ontology networks can help ontology engineers not only to understand the structural complexity of ontologies, but also to evaluate their quality with respect to well-established modular design principles.
Miloš Savić, Mirjana Ivanović, Lakhmi C. Jain

Co-authorship Networks: Social Networks of Research Collaboration

Frontmatter
Chapter 5. Co-authorship Networks: An Introduction
Abstract
In this chapter we introduce and formally define co-authorship networks. Formal definitions of co-authorship networks as undirected graphs, directed graphs and hypergraphs are given. Different schemes to assign weights to co-authorship links are also discussed. Then, we give a classification of co-authorship networks according to the type of research collaboration they represent. Finally, the main applications of co-authorship networks are outlined.
Miloš Savić, Mirjana Ivanović, Lakhmi C. Jain
Chapter 6. Extraction of Co-authorship Networks
Abstract
The extraction of a co-authorship network from a set of bibliographic records in which articles and authors are uniquely identified is an easily solvable problem. However, in a vast majority of bibliographic databases authors are identified by their names. This causes the problem of correct identification of nodes in co-authorship networks due to ambiguous author names. In this chapter we present an overview of initial-based, heuristic and machine learning approaches to the name disambiguation problem. Then, we study the performance of various string similarity measures for detecting name synonyms in bibliographic records. After that, we propose a novel method for disambiguating author names that is based on reference similarity networks and community detection techniques. Finally, we present a case study investigating the impact of name disambiguation on the structure of co-authorship networks.
Miloš Savić, Mirjana Ivanović, Lakhmi C. Jain
Chapter 7. Analysis of Co-authorship Networks
Abstract
Scientific collaboration can be quantitatively studied by analyzing the structure and evolution of co-authorship networks. In this chapter we present a comprehensive overview of research studies focused on empirical analysis of co-authorship networks. Typical structural and evolutionary characteristics of co-authorship networks are identified by an aggregate analysis of examined studies.
Miloš Savić, Mirjana Ivanović, Lakhmi C. Jain
Chapter 8. Analysis of Enriched Co-authorship Networks: Methodology and a Case Study
Abstract
The nodes of an enriched co-authorship network are annotated with various types of nominal and numeric attributes that provide additional information about researchers present in the network. In this chapter we propose a novel methodology to study the structure and evolution of enriched co-authorship networks. The proposed methodology is illustrated on an enriched co-authorship network encompassing researchers employed at one large faculty of sciences.
Miloš Savić, Mirjana Ivanović, Lakhmi C. Jain
Metadaten
Titel
Complex Networks in Software, Knowledge, and Social Systems
verfasst von
Miloš Savić
Mirjana Ivanović
Prof. Lakhmi C. Jain
Copyright-Jahr
2019
Electronic ISBN
978-3-319-91196-0
Print ISBN
978-3-319-91194-6
DOI
https://doi.org/10.1007/978-3-319-91196-0