An analysis of social network-based Sybil defenses
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Recently, there has been much excitement in the research community over using social networks to mitigate multiple identity, or Sybil, attacks. A number of schemes have been proposed, but they differ greatly in the algorithms they use and in the networks upon which they are evaluated. As a result, the research community lacks a clear understanding of how these schemes compare against each other, how well they would work on real-world social networks with different structural properties, or whether there exist other (potentially better) ways of Sybil defense.Keywords:
Sybil attack
Social Network Analysis
Social network (sociolinguistics)
Social network analysis is becoming a prominent data analytics method. In the big data era, high volumes of a wide variety of valuable data can be easily generated or collected at a high velocity. By using advanced social network analysis, researchers from a range of disciplines can analyze large-scale datasets to learn about relationships underlying social networks and community structures by analyzing the linkage patterns among individuals. In this article, the authors address the basic concepts of social network and analytics as well as the applications of social network analysis methods in big data analysis.
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Abstract The increased popularity and improved accessibility of social network analysis has improved our ability to test hypotheses about the complexity of animal social structure. To gain a deeper understanding of the use and application of social network analysis, we systematically surveyed the literature and extracted information on publication trends from articles using social network analysis. We synthesize trends in social network research over time and highlight variation in the use of different aspects of social network analysis. Our primary finding highlights the increase in use of social network analysis over time and from this finding, we observed an increase in the number of review and methods of social network analysis. We also found that most studies included a relatively small number (median = 15, range = 4–1406) of individuals to generate social networks, while the number and type of social network metrics calculated in a given study varied zero to nine (median = 2, range 0–9). The type of data collection or the software programs used to analyze social network data have changed; SOCPROG and UCINET have been replaced by various R packages over time. Finally, we found strong taxonomic and conservation bias in the species studied using social network analysis. Most species studied using social networks are mammals (111/201, 55%) or birds (47/201, 23%) and the majority tend to be species of least concern (119/201, 59%). We highlight emerging trends in social network research that may be valuable for distinct groups of social network researchers: students new to social network analysis, experienced behavioural ecologists interested in using social network analysis, and advanced social network users interested in trends of social network research. In summary we address the temporal trends in social network publication practices, highlight potential bias in some of the ways we employ social network analysis, and provide recommendations for future research based on our findings.
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The opinion leaders of the network dominate the flow of a fairly large number of information,so it can be understand the propagation structure of the microblog platform by analyzing the relationship between them.This paper uses the data which collected in the Sina-microblog for analysis and processing by using the two-step cluster,so this can identify the opinion leaders in groups with social network features.Then,it can use the identified groups of opinion leaders to construct a network by social network analysis,and analyze the center and density of the network.It shows that the relationship of members of the network is not tight enough;but,it will be known that if the two ones who are not directly related need not so long to establish a connection,and identify the core members of the network.
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Social network is a reflection of a social structure, composed of actors and one or several kinds of their mutual ties. Social Network Analysis (SNA), matured in 1970s, is a piece of art and technology quantitatively analyzing social relations. Dynamic Network Analysis (DNA), proposed in 2002, integrates Social Network Analysis, Link Prediction, Multi-Agent Simulation methods and technology, and its core is the information collection, data processing and relationship forecasts and so on. Super network, proposed in the same year, refers to the above and beyond existing network, which has characteristics as multi-layer, multi-level, multi-dimensional, multi-attribute, congestion, and coordination. We have the techniques, tools and indicators of the three network analysis methods collated and summarized, and applied in opinion leader identification. The results proved that to identify and judge opinion leaders, especially the negative ones, with social network analysis, dynamic network analysis and super network analysis played a certain role in controlling network development, from the network behavior.
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A social network is a set of people or other social entities which are connected by set of social relationships such as friendship, interests, behaviour or information exchange. Social network analysis focuses on the analysis of the pattern of relationships among people, organizations, states and such social entities. This paper presents the overview of social network analysis starting from the introduction and the methods to represent social network by different means like matrix, formal methods and graphs and then followed by social network metrics. To analyze and mine the information from such network, we have various tools used for the analysis and visualization of social network.
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A social network is a social structure made up of a set of actors (such as individuals or organizations) and the dyadic ties between these actors. The social network perspective provides a clear way of analyzing the structure of whole social entities . Social network analysis (SNA) is the methodical analysis of social networks. Social network analysis views social relationships in terms of network theory, consisting of nodes (representing individual actors within the network) and ties (which represent relationships between the individuals, such as friendship, kinship, organizational position, sexual relationships, etc.) we show how. Social network analysis (SNA) can be used in indormation retrival system .
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Social network analysis provides a set of theories, concepts, methods, techniques, and instruments for the study of social structure. This paper presents social network analysis as a research method focusing on whole network approach. A review of social network theory is briefly presented and network analysis as a research method is discussed by describing network data, data collection methods, measures and indicators and their analysis and interpretations. A study of online Free/Open Source Software Communities is presented as an example of the use of the methodology. In this sample, the method of collecting online network data is also explored. Overall, the paper shows how to use social network analysis for the study of social structure.
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Social Network Analytics: Computational Research Methods and Techniques focuses on various technical concepts and aspects of social network analysis. The book features the latest developments and findings in this emerging area of research. In addition, it includes a variety of applications from several domains, such as scientific research, and the business and industrial sectors. The technical aspects of analysis are covered in detail, including visualizing and modeling, network theory, mathematical models, the big data analytics of social networks, multidimensional scaling, and more. As analyzing social network data is rapidly gaining interest in the scientific research community because of the importance of the information and insights that can be culled from the wealth of data inherent in the various aspects of the network, this book provides insights on measuring the relationships and flows between people, groups, organizations, computers, URLs, and more. Examines a variety of data analytic techniques that can be applied to social networksDiscusses various methods of visualizing, modeling and tracking network patterns, organization, growth and changeCovers the most recent research on social network analysis and includes applications to a number of domains
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A social network is a relational connection between individuals, including any relationships related to the exchange of information such as those among friends or colleagues. The most significant problem associated with social networks is establishing the role of key individuals, who are charged with conveying important messages among everyone involved. However, the most appropriate representatives are difficult to identify. The aim of this study was to develop a method with which to facilitate the automatic pre-construction of a social network prior to any interaction and pre-identify representatives within the network. The goals of this research were: (1) construct a social network based on a self-organization maps and social network analysis, (2) verify differences between pre-constructed and actual social networks, and (3) identify key representatives and validate the efficiency of the social network.
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