1. In social networks analysis, two different approaches have predominated in creating null models for hypothesis testing, namely pre-network and node network permutation approaches. Although the pre-network permutation approach appears more advantageous, its use has mainly been restricted to data on associations and sampling methods such as ‘group follows.’
2. The pre-network permutation approach has recently been adapted to data on interactions and the focal sampling method, but its performance in different scenarios has not been thoroughly explored. Here, we assessed the performance of the pre- and node network permutation approach in several simulated scenarios based on proneness to false positive or false negatives and with or without observation bias.
3. Our results showed that the pre-network permutation was sensitive to false positives in scenarios with or without observation bias. The node network permutation approach produced fewer false positives and negatives than the pre-network approach, but only in scenarios without observation bias. In scenarios with observation bias, the node network permutation approach was outperformed by pre-network permutation. 
4. Caution should be taken when using the pre- and node network permutations to create null models with data collected via focal sampling. This study provides future methodological research perspectives for social network analyses.

This is the code used to generate the simulated data, make the data analysis, and create the figures of the MS: "Null models for animal social network analysis and data collected via focal sampling: pre- or network permutation?"

All files are written in R statiscial software.