Presenting novel application-based centrality measures for finding important users based on their activities and social behavior

There are more important relationships based on users' behavior and the done activities than those of friendship in online social networks. Study of social behavior of users in these networks has many applications. Analyzing online social networks' activity graphs, as a better representation of users' social behavior, may open new perspectives for real applications such as finding important users. Although detecting these influential nodes based on their friendship relationships is studied a lot, finding important nodes using users' behavior and activates has not attracted much attention. In this work, we study users' importance in various Facebook activity networks including like, comment, post, share, and mixed, then compare gained rankings with those of the friendship network and conclude that users influence analysis in activity networks represents very different results. Afterwards, we propose new centrality measures that can present different rankings suitable for different applications, further to have the potential for simultaneous consideration of various activities in a multilayer network. Experimental results highlights the benefits of using the presented methods. To the best of our knowledge, our methods are the first and only proposed centrality measures that can present different rankings for various applications based on users' social behavior.