Towards User Engagement Dynamics in Social Networks

TL;DR

This paper introduces a novel framework for analyzing user engagement dynamics in social networks by measuring influence through collapsed and anchored power, capturing how user engagement changes affect the network.

Contribution

It proposes new concepts of collapsed and anchored power to quantify user influence during engagement fluctuations and develops efficient algorithms for dynamic network analysis.

Findings

The model effectively captures engagement influence in evolving social networks.

The online algorithms are approximately 1000 times faster than offline re-computation.

Experiments validate the accuracy and efficiency of the proposed methods.

Abstract

The engagement of each user in a social network is an essential indicator for maintaining a sustainable service. Existing studies use the $coreness$ of a user to well estimate its static engagement in a network. However, when the engagement of a user is weakened or strengthened, the influence on other users' engagement is unclear. Besides, the dynamic of user engagement has not been well captured for evolving social networks. In this paper, we systematically study the network dynamic against the engagement change of each user for the first time. The influence of a user is monitored via two novel concepts: the $collapsed~power$ to measure the effect of user weakening, and the $anchored~power$ to measure the effect of user strengthening. We show that the two concepts can be naturally integrated such that a unified offline algorithm is proposed to compute both the collapsed and anchored followers for each user. When the network structure evolves, online techniques are designed to maintain the users' followers, which is faster than redoing the offline algorithm by around 3 orders of magnitude. Extensive experiments on real-life data demonstrate the effectiveness of our model and the efficiency of our algorithms.