Genetically Optimized Realistic Social Network Topology Inspired by Facebook

Abstract

Social network analysis is receiving an increased interest from multiple fields of science since more and more natural and synthetic networks are found to share similar features which help us understand their underlying topological properties. One desire is to create a model of the human society, however, the complexity of such a model is increased by the nature of human interaction, and present studies fail to create a fully realistic model of the societies we live in. Our approach is inspired from studies of online social networking and the ability of genetic algorithms (GA) to optimize topological data in a natural manner. We combine the properties of the small-world and scale-free models to create a community-based social network, which is then rearranged using empirically obtained data from Facebook friendship networks, and optimized using GAs. As a result, our synthetically generated social network topologies are more realistic, with a proposed realism fidelity metric that is with 63% closer to the observed real-world parameters than the best existing model.