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Author: Meghanathan, Natarajan Publisher: IGI Global ISBN: 1466699655 Category : Computers Languages : en Pages : 461
Book Description
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.
Author: Meghanathan, Natarajan Publisher: IGI Global ISBN: 1466699655 Category : Computers Languages : en Pages : 461
Book Description
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.
Author: Dmitry Zinoviev Publisher: Pragmatic Bookshelf ISBN: 1680505408 Category : Computers Languages : en Pages : 343
Book Description
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.
Author: Meghanathan, Natarajan Publisher: IGI Global ISBN: 1522538038 Category : Computers Languages : en Pages : 183
Book Description
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.
Author: Ivan Zelinka Publisher: Springer ISBN: 3662556634 Category : Technology & Engineering Languages : en Pages : 312
Book Description
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.
Author: Katharina A. Zweig Publisher: Springer Science & Business Media ISBN: 3709107415 Category : Computers Languages : en Pages : 535
Book Description
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.
Author: Sharevski, Filipo Publisher: IGI Global ISBN: 152255856X Category : Computers Languages : en Pages : 337
Book Description
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.
Author: Evangelos Kranakis Publisher: Springer Science & Business Media ISBN: 3642309038 Category : Mathematics Languages : en Pages : 415
Book Description
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.
Author: Grigori Sidorov Publisher: Springer ISBN: 3319270605 Category : Computers Languages : en Pages : 575
Book Description
The two volume set LNAI 9413 + LNAI 9414 constitutes the proceedings of the 14th Mexican International Conference on Artificial Intelligence, MICAI 2015, held in Cuernavaca, Morelos, Mexico, in October 2015. The total of 98 papers presented in these proceedings was carefully reviewed and selected from 297 submissions. They were organized in topical sections named: natural language processing; logic and multi-agent systems; bioinspired algorithms; neural networks; evolutionary algorithms; fuzzy logic; machine learning and data mining; natural language processing applications; educational applications; biomedical applications; image processing and computer vision; search and optimization; forecasting; and intelligent applications.
Author: Meghanathan, Natarajan
Author: Meghanathan, Natarajan
Author: Dmitry Zinoviev
Author: Meghanathan, Natarajan
Author: Ivan Zelinka
Author: Sean Meyn
Author: Katharina A. Zweig
Author: Laura Chappell
Author: Sharevski, Filipo
Author: Evangelos Kranakis
Author: Grigori Sidorov