A comparative social network analysis of wasp colonies and classrooms: Linking network structure to functioning
Introduction
In recent years, network science has become an important tool for studying complex systems. Both the global topology of whole systems and the local patterns of interactions within them can be characterised by suitable network indices (Estrada, 2007, Scotti et al., 2007). In fact, holistic approaches are now more exact than ever before: we are able to quantify to what extent everything is connected to everything else, we have techniques for the identification of critically important nodes in networks and we can quantify and compare the topology (“shape”) of different networks. Network properties help us to study the dynamics of complex systems and to make predictions about the behaviour of such systems and their components.
Recent studies on the topological characteristics of interaction networks in bottlenose dolphins (Lusseau, 2003) and guppies (Croft et al., 2004) have provided new insights into the behaviour of these animals: network analysis has acted as an interesting tool for answering questions regarding the behavioural repertoire and population structure of these species and others (Croft et al., 2008).
There is also an increasingly rich literature on comparative network analysis including social networks of animals (e.g. Faust and Skvoretz, 2002, Wey et al., 2008), reviving very old applications of network science in behavioural ecology (cf. Oster and Wilson, 1978). In this paper, we characterise the dominant-subordinate interaction networks of two social wasp species and friendship networks of children in classrooms and compare them. In order to ensure comparability, we only study the basic topology, excluding more system-specific information. We are particularly interested in two system-level properties of these networks: centralisation and small world character. An important question in network science is how structure is linked to function in complex systems, particularly how the topology and dynamics of networks are related. It has been demonstrated that dramatic changes of topology accompany major switches in functioning of some networks (an example is given by Krebs, 2002 for terrorist networks: large global changes precede local action). These large-scale, macroscopic properties give a general insight into the functioning of the system.
Section snippets
Materials and methods
We have studied the structural properties of 84 interaction networks described in 48 primitively eusocial wasp colonies (queen-right and queen-less stages of 12 colonies of Ropalidia marginata and 12 colonies of Ropalidia cyathiformis) and 36 classrooms (four classes in nine time steps). Fig. 1 shows three representative networks.
Results
Here we study global network properties such as centrality (NCID) and small world-like character (SW) of the wasp and children networks (see Appendix for results), while local network properties characterising the position of individual nodes will be discussed elsewhere (Bhadra et al., in preparation).
Discussion
We have compared 84 social networks describing the interaction systems of 48 wasp dominance networks and 36 friendship networks of children. We have found that social networks in classrooms clearly differ from wasp colonies, based on both centrality and the small world character. However, in some cases one of the wasp species has an interaction network more similar to human social networks than to the networks of the other species. For example, based on centralisation, RmQR networks are closer
Acknowledgments
FJ is grateful to 8 teachers and 273 students for providing help and answers, respectively, in the longitudinal social network survey. FJ is fully supported by a Society in Science: the Branco Weiss Fellowship, ETH Zürich, Switzerland. The work on wasps was supported by grants from the Department of Science and Technology, Department of Biotechnology and the Ministry of Environment and Forests, Government of India (to RG) and a CSIR Senior Research Fellowship to AB. Behavioural observations on
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