a synthetic data generator for online social network graphs

Simulation results show our resultant graph to be a close representation of real-life SN graphs with users’ attributes. In addition, we validated our model by comparing its measures with the publicly available real-life SN datasets and previous SN evolution models. We evaluated the resultant synthetic graph by analyzing its structural properties. In our proposed model, users’ attributes and similarities are utilized to synthesize SN graphs. The data generated by these models is suitable to evaluate SN models that are structure dependent but not suitable to evaluate models which depend on the SN users’ attributes and similarities. These models are based on SN structural properties such as preferential attachment. Various SN evolution models are proposed to approximate the real-life SN graphs in previous research. One way to get SN data is to generate synthetic data by using SN evolution models. Simulation and evaluation of models for large-scale SN applications need large datasets. The model is dynamic and sustainable to changes in input parameters, such as number of nodes and nodes’ attributes, by conserving its structural properties. The synthetic graph generated by our model is scale-invariant and has symmetric relationships. Homophily is one of the key factors for interactive relationship formation in SN. Our model enables the SN researchers to generate SN synthetic data for the evaluation of multi-facet SN models that are dependent on users’ attributes and similarities. In this paper, we propose a new scale-free social networks (SNs) evolution model that is based on homophily combined with preferential attachments. Simulation results show our resultant graph to be a close representation of real-life SN graphs with users' attributes. In our proposed model, users' attributes and similarities are utilized to synthesize SN graphs. The data generated by these models is suitable to evaluate SN models that are structure dependent but not suitable to evaluate models which depend on the SN users' attributes and similarities.

The model is dynamic and sustainable to changes in input parameters, such as number of nodes and nodes' attributes, by conserving its structural properties. Our model enables the SN researchers to generate SN synthetic data for the evaluation of multi-facet SN models that are dependent on users' attributes and similarities.

A synthetic data generator for online social network graphs