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Political science is fascinated with networks. This fascination builds on networks' descriptive appeal, and descriptions of networks play a prominent role in recent forays into network analysis. For some time, quantitative research has included node-level measures of network characteristics in standard regression models, thereby incorporating network concepts into familiar models. This approach represents an early advance for the literature but may (a) ignore fundamental theoretical contributions that can be found in a more structurally oriented network perspective, (b) focus attention on superficial aspects of networks as they feed into empirical work, and (c) present the network perspective as a slight tweak to standard models that assume complete independence of all relevant actors. We argue that network analysis is more than a tweak to the status quo ante; rather, it offers a means of addressing one of the holy grails of the social sciences: effectively analyzing the interdependence and flows of influence among individuals, groups, and institutions. Adapted from the source document.

International Journal of Advanced Computer Science and Applications(IJACSA), 9(10), 2018 ; In recent era, networks of data are growing massively and forming a shape of complex structure. Data scientists try to analyze different complex networks and utilize these networks to understand the complex structure of a network in a meaningful way. There is a need to detect and identify such a complex network in order to know how these networks provide communication means while using the complex structure. Social network analysis provides methods to explore and analyze such complex networks using graph theories, network properties and community detection algorithms. In this paper, an analysis of co-authorship network of Public Relation and Public Administration subjects of Microsoft Academic Graph (MAG) is presented, using common centrality measures. The authors belong to different research and academic institutes present all over the world. Cohesive groups of authors have been identified and ranked on the basis of centrality measures, such as betweenness, degree, page rank and closeness. Experimental results show the discovery of authors who are good in specific domain, have a strong field knowledge and maintain collaboration among their peers in the field of Public Relations and Public Administration. ; http://thesai.org/Downloads/Volume9No10/Paper_40-Analysis_of_Coauthorship_Network.pdf

Scientific debates in modern societies often blur the lines between the science that is being debated and the political, moral, and legal implications that come with its societal applications. This manuscript traces the origins of this phenomenon to professional norms within the scientific discipline and to the nature and complexities of modern science and offers an expanded model of science communication that takes into account the political contexts in which science communication takes place. In a second step, it explores what we know from empirical work in political communication, public opinion research, and communication research about the dynamics that determine how issues are debated and attitudes are formed in political environments. Finally, it discusses how and why it will be increasingly important for science communicators to draw from these different literatures to ensure that the voice of the scientific community is heard in the broader societal debates surrounding science.

Scientific debates in modern societies often blur the lines between the science that is being debated and the political, moral, and legal implications that come with its societal applications. This manuscript traces the origins of this phenomenon to professional norms within the scientific discipline and to the nature and complexities of modern science and offers an expanded model of science communication that takes into account the political contexts in which science communication takes place. In a second step, it explores what we know from empirical work in political communication, public opinion research, and communication research about the dynamics that determine how issues are debated and attitudes are formed in political environments. Finally, it discusses how and why it will be increasingly important for science communicators to draw from these different literatures to ensure that the voice of the scientific community is heard in the broader societal debates surrounding science.

Citizens minimize information costs by obtaining political guidance from others who have already assumed the costs of acquiring and processing political information. A problem occurs because ideal informants, typically characterized by the joint presence of political expertise and shared viewpoints, are frequently unavailable or rare within the groups where individuals are located. Hence, individuals must often look beyond their own group boundaries to find such informants. The problem is that obtaining information from individuals located beyond their own groups produces additional costs. Moreover, the availability of ideal informants varies across groups and settings, with the potential to produce (1) context‐dependent patterns of informant centrality, which in turn generate (2) varying levels of polarization among groups and (3) biases in favor of some groups at the expense of others. The article's analysis is based on a series of small‐group experiments, with aggregate implications addressed using a simple agent‐based model.

The aim of the paper is to identify factors responsible for gaining political and economic advantage in network society. The hypothesis is that network communication accounts for political and economic advantage. The Internet network and networking technologies provide new capabilities to new, network form of any system: political, economic (network economy), and social. To formulate findings and verify the hypothesis, the author draws on her own experience, the observation of political and social events, as well as analysis of media content, statistics and findings of the scientific research conducted by experts.

AbstractInteractions between units in political systems often occur across multiple relational contexts. These relational systems feature interdependencies that result in inferential shortcomings and poorly-fitting models when ignored. General advancements in inferential network analysis have improved our ability to understand relational systems featuring interdependence, but developments specific to working with interdependence that cross relational contexts remain sparse. In this paper, I introduce a multilayer network approach to modeling systems comprising multiple relations using the exponential random graph model. In two substantive applications, the first a policy communication network and the second a global conflict network, I demonstrate that the multilayer approach affords inferential leverage and produces models that better fit observed data.

Although online political incivility has increasingly become an object of scholarly inquiry, there exists little agreement on the construct's precise definition. The goal of this work was therefore to explore the relational dynamics among previously identified dimensions of online political incivility. The results of a regularized partial correlation network indicated that a communicator's desire to exclude attitude-discrepant others from discussion played an especially influential role in the variable network. The data also suggested that certain facets of incivility may be likely to be deployed together. Specifically, the data suggested the existence of two identifiable groupings of incivility factors: (1) variables pertaining to violation of speech-based norms and (2) variables pertaining to the violation of the inclusion-based norms that underlie democratic communication processes. These results are discussed in the context of political discussion and deliberation.