Social network analyses allow studying the processes underlying the associations between individuals and the consequences of those associations. Constructing and analysing social networks can be challenging, especially when designing new studies as researchers are confronted with decisions about how to collect data and construct networks, and the answers are not always straightforward. The current lack of guidance on building a social network for a new study system might lead researchers to try several different methods, and risk generating false results arising from multiple hypotheses testing. Here, we suggest an approach for making decisions when starting social network research in a new study system that avoids the pitfall of multiple hypotheses testing. We argue that best edge definition for a network is a decision that can be made using a priori knowledge about the species, and that is independent from the hypotheses that the network will ultimately be used to evaluate. We illustrate this approach with a study conducted on a colonial cooperatively breeding bird, the sociable weaver. We first identified two ways of collecting data using different numbers of feeders and three ways to define associations among birds. We then evaluated which combination of data collection and association definition maximised (i) the assortment of individuals into previously known ‘breeding groups’ (birds that contribute towards the same nest and maintain cohesion when foraging), and (ii) socially differentiated relationships (more strong and weak relationships than expected by chance). This evaluation of different methods based on a priori knowledge of the study species can be implemented in a diverse array of study systems and makes the case for using existing, biologically meaningful knowledge about a system to help navigate the myriad of methodological decisions about data collection and network inference.

The dataset was collected from a population of sociable weavers at Benfontein Nature Reserve, situated ca. 6 km south-east of Kimberley, in the Northern Cape Province, South Africa. We used artificial feeding stations and RFID technology to collect social network data.

There are 7 RData files containing the functions and the datasets used in the manuscript. There is a dataset for each colony, except colony 11 and colony 20 that have 2 datasets each (for the 2 different data collection setups) as explained in the manuscript.

There is an R script example that contains all the steps needed to replicate the analyses of the manuscript. The script has comments to help guide the readers.

If you have any questions contact: andrem343@hotmail.com or andremcferreira@cibio.up.pt.