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      Using Social Network Measures in Wildlife Disease Ecology, Epidemiology, and Management

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          Abstract

          Contact networks, behavioral interactions, and shared use of space can all have important implications for the spread of disease in animals. Social networks enable the quantification of complex patterns of interactions; therefore, network analysis is becoming increasingly widespread in the study of infectious disease in animals, including wildlife. We present an introductory guide to using social-network-analytical approaches in wildlife disease ecology, epidemiology, and management. We focus on providing detailed practical guidance for the use of basic descriptive network measures by suggesting the research questions to which each technique is best suited and detailing the software available for each. We also discuss how using network approaches can be used beyond the study of social contacts and across a range of spatial and temporal scales. Finally, we integrate these approaches to examine how network analysis can be used to inform the implementation and monitoring of effective disease management strategies.

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          Most cited references48

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          Modularity and community structure in networks

          M. Newman (2006)
          Many networks of interest in the sciences, including a variety of social and biological networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure has attracted considerable recent attention. One of the most sensitive detection methods is optimization of the quality function known as "modularity" over the possible divisions of a network, but direct application of this method using, for instance, simulated annealing is computationally costly. Here we show that the modularity can be reformulated in terms of the eigenvectors of a new characteristic matrix for the network, which we call the modularity matrix, and that this reformulation leads to a spectral algorithm for community detection that returns results of better quality than competing methods in noticeably shorter running times. We demonstrate the algorithm with applications to several network data sets.
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            Uncovering the overlapping community structure of complex networks in nature and society

            Many complex systems in nature and society can be described in terms of networks capturing the intricate web of connections among the units they are made of. A key question is how to interpret the global organization of such networks as the coexistence of their structural subunits (communities) associated with more highly interconnected parts. Identifying these a priori unknown building blocks (such as functionally related proteins, industrial sectors and groups of people) is crucial to the understanding of the structural and functional properties of networks. The existing deterministic methods used for large networks find separated communities, whereas most of the actual networks are made of highly overlapping cohesive groups of nodes. Here we introduce an approach to analysing the main statistical features of the interwoven sets of overlapping communities that makes a step towards uncovering the modular structure of complex systems. After defining a set of new characteristic quantities for the statistics of communities, we apply an efficient technique for exploring overlapping communities on a large scale. We find that overlaps are significant, and the distributions we introduce reveal universal features of networks. Our studies of collaboration, word-association and protein interaction graphs show that the web of communities has non-trivial correlations and specific scaling properties.
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              Constructing, conducting and interpreting animal social network analysis

              Summary Animal social networks are descriptions of social structure which, aside from their intrinsic interest for understanding sociality, can have significant bearing across many fields of biology. Network analysis provides a flexible toolbox for testing a broad range of hypotheses, and for describing the social system of species or populations in a quantitative and comparable manner. However, it requires careful consideration of underlying assumptions, in particular differentiating real from observed networks and controlling for inherent biases that are common in social data. We provide a practical guide for using this framework to analyse animal social systems and test hypotheses. First, we discuss key considerations when defining nodes and edges, and when designing methods for collecting data. We discuss different approaches for inferring social networks from these data and displaying them. We then provide an overview of methods for quantifying properties of nodes and networks, as well as for testing hypotheses concerning network structure and network processes. Finally, we provide information about assessing the power and accuracy of an observed network. Alongside this manuscript, we provide appendices containing background information on common programming routines and worked examples of how to perform network analysis using the r programming language. We conclude by discussing some of the major current challenges in social network analysis and interesting future directions. In particular, we highlight the under‐exploited potential of experimental manipulations on social networks to address research questions.
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                Author and article information

                Journal
                Bioscience
                Bioscience
                bioscience
                Bioscience
                Oxford University Press
                0006-3568
                1525-3244
                01 March 2017
                01 February 2017
                01 February 2017
                : 67
                : 3
                : 245-257
                Affiliations
                [1]Matthew J. Silk ( matthewsilk@ 123456outlook.com ) and Robbie A. McDonald (r.mcdonald@exeter.ac.uk) are affiliated with the Environment and Sustainability Institute at the University of Exeter, in Penryn, Cornwall, United Kingdom. Darren P. Croft is with the Centre for Research in Animal Behaviour at the University of Exeter, in the United Kingdom. Richard J. Delahay is affiliated with the National Wildlife Management Centre of the Animal and Plant Health Agency at Woodchester Park, in Gloucestershire, United Kingdom. David J. Hodgson, Mike Boots, and Nicola Weber are with the Centre for Ecology and Conservation at the University of Exeter, in Penryn, Cornwall, United Kingdom; MB is also affiliated with the Department of Integrative Biology at the University of California, Berkeley.
                Article
                biw175
                10.1093/biosci/biw175
                5384163
                28596616
                f91cfd4b-c2a5-4160-afa3-0ad28bd5cce7
                © The Author(s) 2017. Published by Oxford University Press on behalf of the American Institute of Biological Sciences.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Page count
                Pages: 13
                Funding
                Funded by: NERC
                Award ID: NE/M004546/1
                Categories
                Overview Articles

                disease management,super-spreader,network metric,modularity,dynamic network

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