A synthetic data generator for online social network graphs

Two of the difficulties for data analysts of online social networks are (1) the public availability of data and (2) respecting the privacy of the users. One possible solution to both of these problems is to use synthetically generated data. However, this presents a series of challenges related to generating a realistic dataset in terms of topologies, attribute values, communities, data distributions, correlations and so on. In the following work, we present and validate an approach for populating a graph topology with synthetic data which approximates an online social network. The empirical tests confirm that our approach generates a dataset which is both diverse and with a good fit to the target requirements, with a realistic modeling of noise and fitting to communities. A good match is obtained between the generated data and the target profiles and distributions, which is competitive with other state of the art methods. The data generator is also highly configurable, with a sophisticated control parameter set for different similarity/diversity levels.