![]() While previous work has mainly focused on measuring influential users, contents, or pages on the overall OSNs, the problem of predicting influencers in OSNs has remained relatively unexplored from a research perspective. Recent results show that measuring and predicting the influence of the OSNs' users has direct applications in economy, politics, health, etc. ![]() The widespread adoption of Online Social Networks (OSNs) and the ever-increasing amount of information produced by their users has led both industrial and academic researchers to focus on how such systems could be influenced. The performance evaluation of the proposed methods shows that they considerably reduce the amount of used space as well as the total number of replicas in comparison to other approaches. the selected friends who have a copy of the user's data. In this way, they segment the user's data and consider the stability of copy‐location, i.e. In this study, the authors provide some solutions to reduce the amount of used space and the total number of replicas to increase data availability. On the other hand, the amount of used space and the total number of replicas must be reduced as much as possible. For this purpose, the user selects some friends and copies his/her data in their space. Although the privacy of data is increased in these networks, authorised friends must have access to the shared data when the user is not online in the network. However, one of the decentralised architectures is peer‐to‐peer network that every user takes the responsibility of storing and managing his/her data. One of the main challenges of centralised social networks is having a central provider that stores the data which imposes some limitations to preserve the privacy of users’ data. While our contribution is interesting on its own and-to the best of our knowledge-unique, it is worth noticing that it also paves the way for further research in this field. For instance, we are able to predict, on average, for each group, around a third of what an ex-post analysis will show being the 10 most influential members of that group. The achieved results show the quality and viability of our approach. We investigate the accuracy of the predictions collecting data concerning the interactions among about 800,000 users from 18 Facebook groups belonging to different categories (i.e., News, Education, Sport, Entertainment, and Work). Our contribution, while rooted in solid rationale and established analytical tools, is also supported by an extensive experimental campaign. In this article, we formulate the Influencers Prediction problem in the context of groups created in OSNs, and we define a general framework and an effective methodology to predict which users will be able to influence the behavior of the other ones in a future time period, based on historical interactions that occurred within the group. Indeed, one of the main characteristics of OSNs is the ability of users to create different groups types, as well as to join groups defined by other users, in order to share information and opinions. While previous work has mainly focused on measuring current influential users, contents, or pages on the overall OSNs, the problem of predicting influencers in OSNs has remained relatively unexplored from a research perspective. The widespread adoption of Online Social Networks (OSNs), the ever-increasing amount of information produced by their users, and the corresponding capacity to influence markets, politics, and society, have led both industrial and academic researchers to focus on how such systems could be influenced. To validate the proposed approaches, we evaluated their performance conducting a set of simulations exploiting a dataset of temporal information concerning the connections to Facebook collected from a set of users. In this paper, we propose the use of linear predictors to address the problem of the availability of user devices and, hence, data in DOSNs. As a result, the availability of data in these systems is strongly affected (or reflected) by the temporal behavior of their users in terms of connections to DOSNs. ![]() In the last years, the increasing popularity of DOSN services has changed the way of how people interact with each other by enabling users to connect to these services at any time by using their personal devices (such as notebooks or smartphones). Indeed, DOSNs exploit the devices of their users to take on and share the tasks needed to provide services such as storing the published data. The understanding of the user temporal behavior is a crucial aspect for all those systems that rely on user resources for daily operations, such as decentralized online social networks (DOSNs).
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