A comparative survey of graph databases and software for social network analytics: The link prediction perspective

ABSTRACT

In recent years, we have witnessed an excessive increase in the amounts of data available on the Web. These data originate mostly from social media applications or social networks and thus they are highly connected. Graph databases are capable of managing these data successfully since they are particularly designed for storing, retrieving, and searching data that is rich in relationships. This chapter aims to provide a detailed literature review of the existing graph databases and software libraries suitable for performing common social network analytic tasks. In addition, a classification of these graph technologies is also proposed, taking into consideration (i) the provided means of storing, importing, exporting, and querying data, (ii) the available algorithms, (iii) the ability to deal with big social graphs, and (iv) the CPU and memory usage of each one of the reported technologies. To compare and evaluate the different graph technologies, we conduct experiments related to the link prediction problem on datasets with diverse amounts of data.