A Monte Carlo Algorithm for Generating Synthetic Social Networks Based on Personality Compatibility

Social structures and interpersonal relationships may be represented in abstract mathematical objects known as social networks. In their simplest form, social networks consist of nodes corresponding to people and links between pairs of nodes corresponding to relationships between those people. Social networks can be constructed by examining actual groups of people and identifying the relationships of interest between them. However, there are circumstances where such empirical social networks are unavailable, or their use would be undesirable. Consequently, methods to generate synthetic social networks that are not identical to real-world networks but have desired structural similarities to them are valuable. A process for generating synthetic social networks based on attributing human personality types to the nodes and then stochastically adding links between nodes based on the compatibility of the nodes' personalities was developed. A Monte Carlo search algorithm finding an effective assignment of personality types to nodes was implemented and tested. This algorithm was evaluated in terms of realism, i.e., the similarity of the generated synthetic social to exemplar real-world social networks, for 14 different real-world social networks using 18 standard quantitative network metrics. Findings were that this approach produces realistic networks.