Heuristic methods for synthesizing realistic social networks based on personality compatibility

Abstract

Social structures and interpersonal relationships may be represented as social networks consisting 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 have been developed. A process for generating synthetic social networks based on assigning human personality types to the nodes and then adding links between nodes based on the compatibility of the nodes' personalities was developed. Two new algorithms, Probability Search and Compatibility-Degree Matching, for finding an effective assignment of personality types to the nodes were developed, implemented, and tested. The two algorithms were 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 20 standard quantitative network metrics. Both search algorithms produced networks that were, on average, more realistic than a standard network generation algorithm that does not use personality, the Configuration Model. The algorithms were also evaluated in terms of computational complexity.

Generating Attribute Similarity Graphs: A User Behavior-Based Approach from Real- Time Microblogging Data on Platform X

“…Another widely adopted approach entails the creation of synthetic social networks if the real-world network is unavailable. Synthetic data modelling involves the generation of synthetic data that replicates the characteristics of real-world data (Agrawal et al, 2024;Faez et al, 2022;Jiang et al, 2022;Nettleton, 2016;O'Neil & Petty, 2019). This allows researchers to examine and assess information without compromising con dentiality or being constrained by the unavailability of data.…”