Networking Services: A Dynamic Network Perspective
Abstract
Social networking services such as Twitter and
Weibo have become integral to online communities,
where millions of users interact daily. A critical
challenge in these platforms is ranking users based
on their vitality—their level of engagement and
activity—accurately and in a timely manner.
Effective
vitality
ranking benefits numerous
stakeholders, including advertisers and platform
operators. However, due to the massive scale and
dynamic nature of social data, developing reliable
ranking mechanisms is technically challenging.
This paper introduces a novel approach to quantify
user vitality by analyzing dynamic user interactions
in social networks. The proposed framework includes
two algorithms: the first uses direct user interaction
metrics, while the second incorporates mutual
influence between users through an iterative
computation model. In addition to ranking, the study
also addresses vitality prediction using a regression
based model. Evaluations on two real-world datasets
demonstrate the effectiveness and efficiency of the
proposed methods in both vitality ranking and
prediction. The outcomes offer substantial value for
applications such as targeted advertising, trend
detection, and enhanced user engagement strategies
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