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As network science and technology continues to gain popularity, it becomes imperative to develop procedures to examine emergent network domains, as well as classical networks, to help ensure their overall optimization. Advanced Methods for Complex Network Analysis features the latest research on the algorithms and analysis measures being employed in the field of network science. Highlighting the application of graph models, advanced computation, and analytical procedures, this publication is a pivotal resource for students, faculty, industry practitioners, and business professionals interested in theoretical concepts and current developments in network domains.
Book Synopsis Advanced Methods for Complex Network Analysis by : Meghanathan, Natarajan
Download or read book Advanced Methods for Complex Network Analysis written by Meghanathan, Natarajan and published by IGI Global. This book was released on 2016-04-07 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: As network science and technology continues to gain popularity, it becomes imperative to develop procedures to examine emergent network domains, as well as classical networks, to help ensure their overall optimization. Advanced Methods for Complex Network Analysis features the latest research on the algorithms and analysis measures being employed in the field of network science. Highlighting the application of graph models, advanced computation, and analytical procedures, this publication is a pivotal resource for students, faculty, industry practitioners, and business professionals interested in theoretical concepts and current developments in network domains.
Construct, analyze, and visualize networks with networkx, a Python language module. Network analysis is a powerful tool you can apply to a multitude of datasets and situations. Discover how to work with all kinds of networks, including social, product, temporal, spatial, and semantic networks. Convert almost any real-world data into a complex network--such as recommendations on co-using cosmetic products, muddy hedge fund connections, and online friendships. Analyze and visualize the network, and make business decisions based on your analysis. If you're a curious Python programmer, a data scientist, or a CNA specialist interested in mechanizing mundane tasks, you'll increase your productivity exponentially. Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience. Starting with simple networks, convert real-life and synthetic network graphs into networkx data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. Get familiar with presentation-quality network visualization tools, both programmable and interactive--such as Gephi, a CNA explorer. Adapt the patterns from the case studies to your problems. Explore big networks with NetworKit, a high-performance networkx substitute. Each part in the book gives you an overview of a class of networks, includes a practical study of networkx functions and techniques, and concludes with case studies from various fields, including social networking, anthropology, marketing, and sports analytics. Combine your CNA and Python programming skills to become a better network analyst, a more accomplished data scientist, and a more versatile programmer. What You Need: You will need a Python 3.x installation with the following additional modules: Pandas (>=0.18), NumPy (>=1.10), matplotlib (>=1.5), networkx (>=1.11), python-louvain (>=0.5), NetworKit (>=3.6), and generalizesimilarity. We recommend using the Anaconda distribution that comes with all these modules, except for python-louvain, NetworKit, and generalizedsimilarity, and works on all major modern operating systems.
Book Synopsis Complex Network Analysis in Python by : Dmitry Zinoviev
Download or read book Complex Network Analysis in Python written by Dmitry Zinoviev and published by Pragmatic Bookshelf. This book was released on 2018-01-19 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: Construct, analyze, and visualize networks with networkx, a Python language module. Network analysis is a powerful tool you can apply to a multitude of datasets and situations. Discover how to work with all kinds of networks, including social, product, temporal, spatial, and semantic networks. Convert almost any real-world data into a complex network--such as recommendations on co-using cosmetic products, muddy hedge fund connections, and online friendships. Analyze and visualize the network, and make business decisions based on your analysis. If you're a curious Python programmer, a data scientist, or a CNA specialist interested in mechanizing mundane tasks, you'll increase your productivity exponentially. Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience. Starting with simple networks, convert real-life and synthetic network graphs into networkx data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. Get familiar with presentation-quality network visualization tools, both programmable and interactive--such as Gephi, a CNA explorer. Adapt the patterns from the case studies to your problems. Explore big networks with NetworKit, a high-performance networkx substitute. Each part in the book gives you an overview of a class of networks, includes a practical study of networkx functions and techniques, and concludes with case studies from various fields, including social networking, anthropology, marketing, and sports analytics. Combine your CNA and Python programming skills to become a better network analyst, a more accomplished data scientist, and a more versatile programmer. What You Need: You will need a Python 3.x installation with the following additional modules: Pandas (>=0.18), NumPy (>=1.10), matplotlib (>=1.5), networkx (>=1.11), python-louvain (>=0.5), NetworKit (>=3.6), and generalizesimilarity. We recommend using the Anaconda distribution that comes with all these modules, except for python-louvain, NetworKit, and generalizedsimilarity, and works on all major modern operating systems.
As network science and technology continues to gain popularity, it becomes imperative to develop procedures to examine emergent network domains, as well as classical networks, to help ensure their overall optimization. Centrality Metrics for Complex Network Analysis: Emerging Research and Opportunities is a pivotal reference source for the latest research findings on centrality metrics and their broader applications for different categories of networks including wireless sensor networks, curriculum networks, social networks etc. Featuring extensive coverage on relevant areas, such as complex network graphs, node centrality metrics, and mobile sensor networks, this publication is an ideal resource for students, faculty, industry practitioners, and business professionals interested in theoretical concepts and current developments in network domains.
Book Synopsis Centrality Metrics for Complex Network Analysis: Emerging Research and Opportunities by : Meghanathan, Natarajan
Download or read book Centrality Metrics for Complex Network Analysis: Emerging Research and Opportunities written by Meghanathan, Natarajan and published by IGI Global. This book was released on 2018-04-05 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: As network science and technology continues to gain popularity, it becomes imperative to develop procedures to examine emergent network domains, as well as classical networks, to help ensure their overall optimization. Centrality Metrics for Complex Network Analysis: Emerging Research and Opportunities is a pivotal reference source for the latest research findings on centrality metrics and their broader applications for different categories of networks including wireless sensor networks, curriculum networks, social networks etc. Featuring extensive coverage on relevant areas, such as complex network graphs, node centrality metrics, and mobile sensor networks, this publication is an ideal resource for students, faculty, industry practitioners, and business professionals interested in theoretical concepts and current developments in network domains.
Evolutionary algorithms constitute a class of well-known algorithms, which are designed based on the Darwinian theory of evolution and Mendelian theory of heritage. They are partly based on random and partly based on deterministic principles. Due to this nature, it is challenging to predict and control its performance in solving complex nonlinear problems. Recently, the study of evolutionary dynamics is focused not only on the traditional investigations but also on the understanding and analyzing new principles, with the intention of controlling and utilizing their properties and performances toward more effective real-world applications. In this book, based on many years of intensive research of the authors, is proposing novel ideas about advancing evolutionary dynamics towards new phenomena including many new topics, even the dynamics of equivalent social networks. In fact, it includes more advanced complex networks and incorporates them with the CMLs (coupled map lattices), which are usually used for spatiotemporal complex systems simulation and analysis, based on the observation that chaos in CML can be controlled, so does evolution dynamics. All the chapter authors are, to the best of our knowledge, originators of the ideas mentioned above and researchers on evolutionary algorithms and chaotic dynamics as well as complex networks, who will provide benefits to the readers regarding modern scientific research on related subjects.
Book Synopsis Evolutionary Algorithms, Swarm Dynamics and Complex Networks by : Ivan Zelinka
Download or read book Evolutionary Algorithms, Swarm Dynamics and Complex Networks written by Ivan Zelinka and published by Springer. This book was released on 2017-11-25 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evolutionary algorithms constitute a class of well-known algorithms, which are designed based on the Darwinian theory of evolution and Mendelian theory of heritage. They are partly based on random and partly based on deterministic principles. Due to this nature, it is challenging to predict and control its performance in solving complex nonlinear problems. Recently, the study of evolutionary dynamics is focused not only on the traditional investigations but also on the understanding and analyzing new principles, with the intention of controlling and utilizing their properties and performances toward more effective real-world applications. In this book, based on many years of intensive research of the authors, is proposing novel ideas about advancing evolutionary dynamics towards new phenomena including many new topics, even the dynamics of equivalent social networks. In fact, it includes more advanced complex networks and incorporates them with the CMLs (coupled map lattices), which are usually used for spatiotemporal complex systems simulation and analysis, based on the observation that chaos in CML can be controlled, so does evolution dynamics. All the chapter authors are, to the best of our knowledge, originators of the ideas mentioned above and researchers on evolutionary algorithms and chaotic dynamics as well as complex networks, who will provide benefits to the readers regarding modern scientific research on related subjects.
From foundations to state-of-the-art; the tools and philosophy you need to build network models.
Book Synopsis Control Techniques for Complex Networks by : Sean Meyn
Download or read book Control Techniques for Complex Networks written by Sean Meyn and published by Cambridge University Press. This book was released on 2008 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: From foundations to state-of-the-art; the tools and philosophy you need to build network models.
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.
Book Synopsis Network Analysis Literacy by : Katharina A. Zweig
Download or read book Network Analysis Literacy written by Katharina A. Zweig and published by Springer Science & Business Media. This book was released on 2016-10-26 with total page 535 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Advanced Network Analysis Techniques by : Laura Chappell
Download or read book Advanced Network Analysis Techniques written by Laura Chappell and published by . This book was released on 2000 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
As well as highlighting potentially useful applications for network analysis, this volume identifies new targets for mathematical research that promise to provide insights into network systems theory as well as facilitating the cross-fertilization of ideas between sectors. Focusing on financial, security and social aspects of networking, the volume adds to the growing body of evidence showing that network analysis has applications to transportation, communication, health, finance, and social policy more broadly. It provides powerful models for understanding the behavior of complex systems that, in turn, will impact numerous cutting-edge sectors in science and engineering, such as wireless communication, network security, distributed computing and social networking, financial analysis, and cyber warfare. The volume offers an insider’s view of cutting-edge research in network systems, including methodologies with immense potential for interdisciplinary application. The contributors have all presented material at a series of workshops organized on behalf of Canada’s MITACS initiative, which funds projects and study grants in ‘mathematics for information technology and complex systems’. These proceedings include papers from workshops on financial networks, network security and cryptography, and social networks. MITACS has shown that the partly ghettoized nature of network systems research has led to duplicated work in discrete fields, and thus this initiative has the potential to save time and accelerate the pace of research in a number of areas of network systems research.
Book Synopsis Advances in Network Analysis and its Applications by : Evangelos Kranakis
Download or read book Advances in Network Analysis and its Applications written by Evangelos Kranakis and published by Springer Science & Business Media. This book was released on 2012-10-24 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: As well as highlighting potentially useful applications for network analysis, this volume identifies new targets for mathematical research that promise to provide insights into network systems theory as well as facilitating the cross-fertilization of ideas between sectors. Focusing on financial, security and social aspects of networking, the volume adds to the growing body of evidence showing that network analysis has applications to transportation, communication, health, finance, and social policy more broadly. It provides powerful models for understanding the behavior of complex systems that, in turn, will impact numerous cutting-edge sectors in science and engineering, such as wireless communication, network security, distributed computing and social networking, financial analysis, and cyber warfare. The volume offers an insider’s view of cutting-edge research in network systems, including methodologies with immense potential for interdisciplinary application. The contributors have all presented material at a series of workshops organized on behalf of Canada’s MITACS initiative, which funds projects and study grants in ‘mathematics for information technology and complex systems’. These proceedings include papers from workshops on financial networks, network security and cryptography, and social networks. MITACS has shown that the partly ghettoized nature of network systems research has led to duplicated work in discrete fields, and thus this initiative has the potential to save time and accelerate the pace of research in a number of areas of network systems research.
Modern communications are now more than ever heavily dependent on mobile networks, creating the potential for higher incidents of sophisticated crimes, terrorism acts, and high impact cyber security breaches. Disrupting these unlawful actions requires a number of digital forensic principles and a comprehensive investigation process. Mobile Network Forensics: Emerging Research and Opportunities is an essential reference source that discusses investigative trends in mobile devices and the internet of things, examining malicious mobile network traffic and traffic irregularities, as well as software-defined mobile network backbones. Featuring research on topics such as lawful interception, system architecture, and networking environments, this book is ideally designed for forensic practitioners, government officials, IT consultants, cybersecurity analysts, researchers, professionals, academicians, and students seeking coverage on the technical and legal aspects of conducting investigations in the mobile networking environment.
Book Synopsis Mobile Network Forensics: Emerging Research and Opportunities by : Sharevski, Filipo
Download or read book Mobile Network Forensics: Emerging Research and Opportunities written by Sharevski, Filipo and published by IGI Global. This book was released on 2018-11-16 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern communications are now more than ever heavily dependent on mobile networks, creating the potential for higher incidents of sophisticated crimes, terrorism acts, and high impact cyber security breaches. Disrupting these unlawful actions requires a number of digital forensic principles and a comprehensive investigation process. Mobile Network Forensics: Emerging Research and Opportunities is an essential reference source that discusses investigative trends in mobile devices and the internet of things, examining malicious mobile network traffic and traffic irregularities, as well as software-defined mobile network backbones. Featuring research on topics such as lawful interception, system architecture, and networking environments, this book is ideally designed for forensic practitioners, government officials, IT consultants, cybersecurity analysts, researchers, professionals, academicians, and students seeking coverage on the technical and legal aspects of conducting investigations in the mobile networking environment.
Technology has become profoundly integrated into modern society; however, this increases the risk of vulnerabilities, such as hacking and other system errors, along with other online threats. Security, Privacy, and Anonymization in Social Networks: Emerging Research and Opportunities is a pivotal reference source for the most up-to-date research on edge clustering models and weighted social networks. Presenting widespread coverage across a range of applicable perspectives and topics, such as neighborhood attacks, fast k-degree anonymization (FKDA), and vertex-clustering algorithms, this book is ideally designed for academics, researchers, post-graduates, and practitioners seeking current research on undirected networks and greedy algorithms for social network anonymization.
Book Synopsis Security, Privacy, and Anonymization in Social Networks: Emerging Research and Opportunities by : Tripathy, B. K.
Download or read book Security, Privacy, and Anonymization in Social Networks: Emerging Research and Opportunities written by Tripathy, B. K. and published by IGI Global. This book was released on 2018-01-19 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: Technology has become profoundly integrated into modern society; however, this increases the risk of vulnerabilities, such as hacking and other system errors, along with other online threats. Security, Privacy, and Anonymization in Social Networks: Emerging Research and Opportunities is a pivotal reference source for the most up-to-date research on edge clustering models and weighted social networks. Presenting widespread coverage across a range of applicable perspectives and topics, such as neighborhood attacks, fast k-degree anonymization (FKDA), and vertex-clustering algorithms, this book is ideally designed for academics, researchers, post-graduates, and practitioners seeking current research on undirected networks and greedy algorithms for social network anonymization.
