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 (GDPR). 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 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 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 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.

In the last few decades sociologists were trying to explain human behaviour by analysing social networks, which requires access to data about interpersonal relationships. This represented a big obstacle in this research field until the emergence of online social networks (OSNs), which vastly facilitated the process of collecting such data. Nowadays, by crawling public profiles on OSNs, it is possible to build a social graph where “friends” on OSN become represented as connected nodes. OSN connection does not necessarily indicate a close real-life relationship, but using OSN interaction records may reveal real-life relationship intensities, a topic which inspired a number of recent researches. Still, published research currently lacks an extensive exploratory analysis of OSN interaction records, i.e. a comprehensive overview of users’ interaction via different ways of OSN interaction. In this paper, we provide such an overview by leveraging results of conducted extensive social experiment which managed to collect records for over 3200 Facebook users interacting with over 1,400,000 of their friends. Our exploratory analysis focuses on extracting population distributions and correlation parameters for 13 interaction parameters, providing valuable insight into OSN interaction for future researches aimed at this field of study.

Online social networks (OSN) are one of the most popular forms of modern communication and among the best known is Facebook. Information about the connection between users on the OSN is often very scarce. It is only known if users are...

In today's world, social networks are one of the most popular ways of communication. Communication and relations among people can be monitored based on interaction on social networks. The question is the exact meaning of this interaction and can the real life relationship be interpreted from interaction on social networks. In this study, we used the most popular social network Facebook with the aim of finding the correlation between the interaction among users on Facebook and friendship in real life. In this paper, we propose a model for calculating the weight of friendship among users of social networks based on their interactions on Facebook. The model takes into account the general significance of a particular form of interaction (like, comment, etc.) and the specific significance of this form of interaction for each user. Apart from creating a model for calculating the weight of friendship, in this paper, general significance ratio of each communication parameter was experimentally determined. The model was built and evaluated by searching intersection of two sets, a set of user's 10 best friends that he himself cited and a set of 10 best friends obtained using the proposed model. Average overlapping of these two sets was 70.9%. Additionally, the overlap level of these two sets for different demographic groups was analyzed.