Modeling Non-Progressive Phenomena for Influence Propagation

TL;DR

This paper introduces a scalable, accurate continuous-time Markov model for influence propagation that accounts for nodes switching between active and inactive states, unlike traditional progressive models.

Contribution

It proposes a novel non-progressive influence model using continuous-time Markov processes, enabling better modeling of real-world influence dynamics.

Findings

Model is 17-20 times faster than existing methods.

Achieves higher accuracy in influence spread estimation.

Scalable to graphs with over 2 million nodes.

Abstract

Recent work on modeling influence propagation focus on progressive models, i.e., once a node is influenced (active) the node stays in that state and cannot become inactive. However, this assumption is unrealistic in many settings where nodes can transition between active and inactive states. For instance, a user of a social network may stop using an app and become inactive, but again activate when instigated by a friend, or when the app adds a new feature or releases a new version. In this work, we study such non-progressive phenomena and propose an efficient model of influence propagation. Specifically, we model in influence propagation as a continuous-time Markov process with 2 states: active and inactive. Such a model is both highly scalable (we evaluated on graphs with over 2 million nodes), 17-20 times faster, and more accurate for estimating the spread of influence, as compared with state-of-the-art progressive models for several applications where nodes may switch states.