We present an unsupervised prediction algorithm to continuously predict group members' departure behaviors in a dynamic information network.
Over time, some users will leave some groups. Thus, for both users and groups, it's meaningful to predict which users would leave which groups. Existing studies ...
It is shown that nodes at the fringe are more likely to depart and subsequent departure are correlated among neighboring nodes in tightly-knit communities, ...
Leave or not leave? Group members' departure prediction in dynamic information networks ; Journal: Information Sciences, 2021, p. 138-156 ; Publisher: Elsevier BV.
Abstract. We proposes a departure behavior prediction algorithm to predict the user's departure behavior. The k-core decomposition is conducted on the graph ...
In this paper, we consider the natural arrival and departure of users in a social network, and ask whether the dynamics of arrival, which have been studied ...
Members leaving one social group may lead to the formation of a new group from the existing one, which results in a group split. Such group splitting events are ...
Who Is Leaving and Why? The Dynamics of High-Quality Human ...
journals.aom.org › doi › amj.2021.1327
This study proposes a unified, dynamic framework based on turnover event theory to evaluate the effects of dismissals, layoff announcements, and voluntary ...
Abstract—In this paper, we propose a time-efficient contributory key agreement framework for secure com- munications in dynamic groups. The proposed scheme.
Dec 2, 2021 · Abstract—Dynamic neural network is an emerging research topic in deep learning. Compared to static models which have fixed.