Analysis of Facebook Interaction as Basis for Synthetic Expanded Social Graph Generation.

Social networks have long been the subject of scientific researches, frequently hindered by the unavailability of representative datasets. The advent of online social networks (OSNs), which store data about interactions between billions of people, has greatly alleviated this problem. Since user interaction on the OSNs can correspond to their real-life relationships, OSN datasets quickly became a highly sought-after resource for social network research. However, enabling open access to such data entails serious security and privacy risks, especially after the introduction of the European General Data Protection Regulation. Some researchers mitigate this problem through anonymization, while others argue for the creation of synthetic datasets. We consider synthetic datasets preferable since they circumvent the security and privacy issues. Existing synthetic dataset generators produce a social graph containing only information whether a pair of nodes are connected. However, interpersonal relationships are much more complex. Because of that, our research considers the possibility of generating a synthetic expanded social graph which, in addition to the information about the existence of a connection between a pair of users, also provides information about the types and intensities of users' interactions. As Facebook is the leading OSN today, we performed an extensive analysis of Facebook users' interaction records with the aim of getting an insight into real-life interaction patterns. In this paper, we present results of this analysis at ego-user level and offer the conceptual solution for synthetic expanded social graph generation, which uses conducted analysis results as its basis.