Priority Rank Model for Social Network Generation

Currently available artificial network generation models are characterized by consistency and low variance due to the rigidity of models' underlying assumptions. Networks generated from these models are usually too regular and do not contain noise and imbalance inherent in networks induced by human behavior. An important consequence is that much research on social network analysis presented in recent years used idealistic artificial networks that did not conform to reality. In order to alleviate this problem we introduce a new network generation model capable of modeling a broad spectrum of networks. In our model, the network formation process is not hard-coded into the model. Rather, we propose a simple mechanism for network creation based on priority ranking, and we encode the guiding principle of network formation as a distance function. By only changing the distance function definition and using the same priority ranking mechanism we are able to model very diverse networks. Our preliminary results show that we can mimic the behavior of popular artificial network generation models, such as the Erdos-Renyi random network model, the Watts-Strogatz small world model, or the Albert-Barabasi preferential attachment model, but we can generate new types of networks as well. Following the principles of Open Science we publish the source code used to perform experiments and publish results in a public repository(1)