Products related to Network:
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Inferential Network Analysis
This unique textbook provides an introduction to statistical inference with network data.The authors present a self-contained derivation and mathematical formulation of methods, review examples, and real-world applications, as well as provide data and code in the R environment that can be customised.Inferential network analysis transcends fields, and examples from across the social sciences are discussed (from management to electoral politics), which can be adapted and applied to a panorama of research.From scholars to undergraduates, spanning the social, mathematical, computational and physical sciences, readers will be introduced to inferential network models and their extensions.The exponential random graph model and latent space network model are paid particular attention and, fundamentally, the reader is given the tools to independently conduct their own analyses.
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Social Network Analysis
Incorporating the most important and cutting-edge developments in the field, this bestselling text introduces newcomers to the key theories and techniques of social network analysis and guides more experienced analysts in their own research. New to This Edition:A chapter on data collection, covering a crucial phase of the research processFully updated examples reiterate the continued importance of social network analysis in an increasingly interconnected worldDetailed ‘Further Reading’ sections help you explore the wider literaturePractical exercises including real-world examples of social networks enable you to apply your learningExpanded and brought right up-to-date, this classic text remains the indispensable guide to social network analysis for students, lecturers and researchers throughout the social sciences.
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Network Security Assessment 3e
How secure is your network? The best way to find out is to attack it, using the same tactics attackers employ to identify and exploit weaknesses.With the third edition of this practical book, you'll learn how to perform network-based penetration testing in a structured manner.Security expert Chris McNab demonstrates common vulnerabilities, and the steps you can take to identify them in your environment.System complexity and attack surfaces continue to grow.This book provides a process to help you mitigate risks posed to your network.Each chapter includes a checklist summarizing attacker techniques, along with effective countermeasures you can use immediately.Learn how to effectively test system components, including: Common services such as SSH, FTP, Kerberos, SNMP, and LDAP Microsoft services, including NetBIOS, SMB, RPC, and RDP SMTP, POP3, and IMAP email services IPsec and PPTP services that provide secure network access TLS protocols and features providing transport security Web server software, including Microsoft IIS, Apache, and Nginx Frameworks including Rails, Django, Microsoft ASP.NET, and PHP Database servers, storage protocols, and distributed key-value stores
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Social Network Analysis : Research Methods
This book introduces the non-specialist reader to the principal ideas, nature and purpose of social network analysis.Social networks operate on many levels, from families up to the level of nations, and play a critical role in determining the way problems are solved, organizations are run, and the degree to which individuals achieve their goals.Social network theory maps these relationships between individual actors and has become hugely influential across the social sciences.Assuming no prior knowledge of quantitative sociology, and avoiding technicalities, this book presents the key ideas in context through examples and illustrations.Using a structured approach to understanding work in this area, John Scott signposts further reading and online sources so that readers can develop their knowledge and skills to become practitioners of this research method.A series of Frequently Asked Questions takes the reader through the main objections raised against social network analysis.The new edition updates the overview of the discipline with more recent work and current research in sociology.
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How does network analysis work in electrical engineering?
Network analysis in electrical engineering involves studying and analyzing the behavior of electrical circuits. This analysis helps in understanding how different components in a circuit interact with each other and how signals flow through the network. By using techniques such as Kirchhoff's laws, nodal analysis, and mesh analysis, engineers can determine important parameters like voltage, current, power, and impedance in a circuit. This information is crucial for designing, optimizing, and troubleshooting electrical systems. Overall, network analysis plays a key role in ensuring the efficient and reliable operation of electrical networks.
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Can I simply move resistors in electrical engineering during network analysis?
In electrical engineering, resistors can be moved during network analysis if they are connected in series or parallel. When resistors are connected in series, they can be moved to different locations within the circuit without affecting the overall resistance. Similarly, resistors connected in parallel can also be moved without changing the total resistance. However, if the resistors are part of a more complex circuit with multiple connections, their movement may affect the overall behavior of the circuit and should be carefully analyzed.
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Which network should one use at home - public network, home network, or work network? Which is the safest?
For optimal security, it is recommended to use a home network at home. Home networks are typically more secure than public networks, as they are password-protected and offer more control over who can access the network. Public networks, such as those found in cafes or airports, are the least secure option as they are open to anyone and can be easily compromised by hackers. Work networks are secure but are typically designed for work-related activities and may have restrictions on personal use.
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Which network should one use at home - public network, home network, or workplace network? Which is the safest?
For optimal security, it is recommended to use a home network at home. Home networks are typically more secure than public networks as they are password-protected and controlled by the user. Workplace networks can also be secure, but they may have additional restrictions and monitoring in place. Public networks, such as those in cafes or airports, are the least secure option as they are open to anyone and can be easily compromised by hackers.
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Complex Network Analysis in Python
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.
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Network Analysis : Integrating Social Network Theory, Method, and Application with R
The size and availability of network information has exploded over the last decade.Social scientists now share the stage of network analysis with computer scientists, physicists, and statisticians.While a number of introductions to network analysis are now available, most focus on theory, methods, or application alone.This book integrates all three. Network Analysis is an introduction to both the why and how of Social Network Analysis (SNA).It presents a broad theoretical overview rooted in social scientific approaches and guides users in how network analysis can answer core theoretical questions.It provides a comprehensive overview of descriptive and analytical approaches, including practical tutorials in R with sample data sets.Using an integrated approach, this book aims to quickly bring novice network researchers up to speed while avoiding common programming and analysis mistakes so that they might gain insight into the fundamental theories, key concepts, and methodological application of SNA.
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Semantic Network Analysis in Social Sciences
Semantic Network Analysis in Social Sciences introduces the fundamentals of semantic network analysis and its applications in the social sciences.Readers learn how to easily transform any given text into a visual network of words co-occurring together, a process that allows mapping the main themes appearing in the text and revealing its main narratives and biases. Semantic network analysis is particularly useful today with the increasing volumes of text-based information available.It is one of the developing, cutting-edge methods to organize, identify patterns and structures, and understand the meanings of our information society.The first chapters in this book offer step-by-step guidelines for conducting semantic network analysis, including choosing and preparing the text, selecting desired words, constructing the networks, and interpreting their meanings.Free software tools and code are also presented. The rest of the book displays state-of-the-art studies from around the world that apply this method to explore news, political speeches, social media content, and even to organize interview transcripts and literature reviews. Aimed at scholars with no previous knowledge in the field, this book can be used as a main or a supplementary textbook for general courses on research methods or network analysis courses, as well as a starting point to conduct your own content analysis of large texts.
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Social Network Analysis : Theory and Applications
SOCIAL NETWORK ANALYSIS As social media dominates our lives in increasing intensity, the need for developers to understand the theory and applications is ongoing as well.This book serves that purpose. Social network analysis is the solicitation of network science on social networks, and social occurrences are denoted and premeditated by data on coinciding pairs as the entities of opinion.The book features: Social network analysis from a computational perspective using python to show the significance of fundamental facets of network theory and the various metrics used to measure the social network. An understanding of network analysis and motivations to model phenomena as networks.Real-world networks established with human-related data frequently display social properties, i.e., patterns in the graph from which human behavioral patterns can be analyzed and extracted. Exemplifies information cascades that spread through an underlying social network to achieve widespread adoption. Network analysis that offers an appreciation method to health systems and services to illustrate, diagnose, and analyze networks in health systems. The social web has developed a significant social and interactive data source that pays exceptional attention to social science and humanities research. The benefits of artificial intelligence enable social media platforms to meet an increasing number of users and yield the biggest marketplace, thus helping social networking analysis distribute better customer understanding and aiding marketers to target the right customers. Audience The book will interest computer scientists, AI researchers, IT and software engineers, mathematicians.
Price: 148.00 £ | Shipping*: 0.00 £
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Which network should one use at home - public network, home network, or work network? Which is the most secure?
For optimal security at home, it is recommended to use the home network. Home networks are typically more secure than public networks as they are private and can be customized with security measures such as strong passwords and encryption. Work networks are also secure, but they are designed for professional use and may have restrictions that limit personal activities. Overall, the home network is the most secure option for personal use.
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Which network should one use at home - public network, home network, or workplace network? Which is the most secure?
For the highest level of security, it is recommended to use a home network at home. Home networks are typically more secure than public networks, as they are controlled and managed by the homeowner. Workplace networks can also be secure, but they may have additional security measures in place that could restrict certain activities. Public networks, such as those found in cafes or airports, are the least secure option as they are open to anyone and can be more easily compromised by hackers.
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What are network internal and selected network flats?
Network internal flats are properties that are only available to employees of a specific company or organization. These flats are usually located within a designated area or building and are reserved for employees as a benefit or perk. Selected network flats, on the other hand, are properties that are part of a network of accommodations available to employees of multiple companies or organizations. These flats may be located in various cities or countries and offer a range of amenities and services for employees traveling for work.
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Which network should one use at home - public network, home network, or workplace network? Which one is the most secure?
For the highest level of security, it is recommended to use a home network at home. Home networks are typically more secure than public networks, as they are controlled and managed by the user. Workplace networks can also be secure, but they may have additional security measures in place that could restrict certain activities. Public networks, such as those found in cafes or airports, are the least secure option as they are open to anyone and can be more easily compromised by hackers.
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