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      Social network theory in the behavioural sciences: potential applications

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          Abstract

          Social network theory has made major contributions to our understanding of human social organisation but has found relatively little application in the field of animal behaviour. In this review, we identify several broad research areas where the networks approach could greatly enhance our understanding of social patterns and processes in animals. The network theory provides a quantitative framework that can be used to characterise social structure both at the level of the individual and the population. These novel quantitative variables may provide a new tool in addressing key questions in behavioural ecology particularly in relation to the evolution of social organisation and the impact of social structure on evolutionary processes. For example, network measures could be used to compare social networks of different species or populations making full use of the comparative approach. However, the networks approach can in principle go beyond identifying structural patterns and also can help with the understanding of processes within animal populations such as disease transmission and information transfer. Finally, understanding the pattern of interactions in the network (i.e. who is connected to whom) can also shed some light on the evolution of behavioural strategies.

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          Most cited references42

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          A simple rule for the evolution of cooperation on graphs and social networks.

          A fundamental aspect of all biological systems is cooperation. Cooperative interactions are required for many levels of biological organization ranging from single cells to groups of animals. Human society is based to a large extent on mechanisms that promote cooperation. It is well known that in unstructured populations, natural selection favours defectors over cooperators. There is much current interest, however, in studying evolutionary games in structured populations and on graphs. These efforts recognize the fact that who-meets-whom is not random, but determined by spatial relationships or social networks. Here we describe a surprisingly simple rule that is a good approximation for all graphs that we have analysed, including cycles, spatial lattices, random regular graphs, random graphs and scale-free networks: natural selection favours cooperation, if the benefit of the altruistic act, b, divided by the cost, c, exceeds the average number of neighbours, k, which means b/c > k. In this case, cooperation can evolve as a consequence of 'social viscosity' even in the absence of reputation effects or strategic complexity.
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            Food-web structure and network theory: The role of connectance and size.

            Networks from a wide range of physical, biological, and social systems have been recently described as "small-world" and "scale-free." However, studies disagree whether ecological networks called food webs possess the characteristic path lengths, clustering coefficients, and degree distributions required for membership in these classes of networks. Our analysis suggests that the disagreements are based on selective use of relatively few food webs, as well as analytical decisions that obscure important variability in the data. We analyze a broad range of 16 high-quality food webs, with 25-172 nodes, from a variety of aquatic and terrestrial ecosystems. Food webs generally have much higher complexity, measured as connectance (the fraction of all possible links that are realized in a network), and much smaller size than other networks studied, which have important implications for network topology. Our results resolve prior conflicts by demonstrating that although some food webs have small-world and scale-free structure, most do not if they exceed a relatively low level of connectance. Although food-web degree distributions do not display a universal functional form, observed distributions are systematically related to network connectance and size. Also, although food webs often lack small-world structure because of low clustering, we identify a continuum of real-world networks including food webs whose ratios of observed to random clustering coefficients increase as a power-law function of network size over 7 orders of magnitude. Although food webs are generally not small-world, scale-free networks, food-web topology is consistent with patterns found within those classes of networks.
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              Detecting community structure in networks

              M. Newman (2004)
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                Author and article information

                Contributors
                +44-113-2332835 , J.Krause@leeds.ac.uk
                Journal
                Behav Ecol Sociobiol
                Behav. Ecol. Sociobiol. (Print)
                Behavioral Ecology and Sociobiology
                Springer-Verlag (Berlin/Heidelberg )
                0340-5443
                1432-0762
                14 July 2007
                2007
                : 62
                : 1
                : 15-27
                Affiliations
                [1 ]GRID grid.9909.9, ISNI 0000000419368403, Institute of Integrative and Comparative Biology, , University of Leeds, ; Leeds, LS2 9JT UK
                [2 ]GRID grid.7362.0, ISNI 0000000118820937, College of Natural Sciences, School of Biological Sciences, , University of Wales Bangor, ; Bangor, Gwynedd LL57 2UW UK
                [3 ]GRID grid.7340.0, ISNI 0000000121621699, Department of Physics, , University of Bath, ; Bath, BA2 7AY UK
                Author notes

                Communicated by A. Cockburn

                Article
                445
                10.1007/s00265-007-0445-8
                7079911
                32214613
                7f2a1ba6-c877-46b6-b7ec-445bd072c310
                © Springer-Verlag 2007

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 22 December 2006
                : 12 May 2007
                : 3 June 2007
                Categories
                Review
                Custom metadata
                © Springer-Verlag 2007

                Ecology
                social networks,social organisation,mate choice,disease transmission,information transfer,cooperation

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