A note on reconstructing animal social networks from independent small-group observations

Animal social networks are often built by aggregating a series of independent observations of two or more members of a group interacting or in association. Every time an observation is made, edges are drawn between each pair of individuals involved. I examined the effect of edge sample size on the reconstruction of social networks. I created different artificial networks and sampled edges from each. I estimated and compared the number of nodes, number of components, path length, clustering coefficient, network density, mean degree, betweenness centrality and degree probability distribution of the reconstructed networks to the true value of the network. I describe how the accuracy of these measures changes as the fraction of sampled edges increases. I show that edge sample size affects network measures in different ways and that when an incomplete sample is analysed, network properties can be considerably misrepresented. I also show that, because animal networks are typically small, simple curve fitting to the degree distribution P(k) should be done with caution, because different curve models can show significant fit for the same data. Overall, the results indicate that strong claims about animal social networks should not be made unless considerable effort has been made to collect an exhaustive number of association/interaction data points. If observations of associations/interactions are accumulated over a long period, the effect of increasing edge sample size could be mistaken for temporal change in social network and could also muddy the comparison of network structure between populations and between species.