Popular Topics Spread Faster: New Dimension for Influence Propagation in Online Social Networks

TL;DR

This paper introduces the Dynamic Influence Propagation (DIP) model for more realistic information spread in OSNs, addressing variable propagation rates and proposing an approximation solution for the complex TAP-DIP problem, validated on real datasets.

Contribution

It proposes the DIP model allowing dynamic propagation rates and develops an approximation algorithm for the TAP-DIP problem, enhancing influence maximization strategies in OSNs.

Findings

The solution produces smaller, high-quality seed sets considering rate changes.

The approach is scalable and effective on real OSN datasets.

Considering DIP significantly impacts seed set selection.

Abstract

Information can propagate among Online Social Network (OSN) users at a high speed, which makes the OSNs become important platforms for viral marketing. Although the viral marketing related problems in OSNs have been extensively studied in the past decade, the existing works all assume known propagation rates and are not able to solve the scenario when the rates may dynamically increase for popular topics. In this paper, we propose a novel model, Dynamic Influence Propagation (DIP), which allows propagation rates to change during the diffusion and can be used for describing information propagation in OSNs more realistically. Based on DIP, we define a new research problem: Threshold Activation Problem under DIP (TAP-DIP). TAP-DIP is more generalized than TAP and can be used for studying the DIP model. However, it adds another layer of complexity over the already \#P-hard TAP problem. Despite it hardness, we are able to approximate TAP-DIP with $O(\log|V|)$ ratio. Our solution consists of two major parts: 1) the Lipschitz optimization technique and 2) a novel solution to the general version of TAP, the Multi-TAP problem. We experimentally test our solution Using various real OSN datasets, and demonstrate that our solution not only generates high-quality yet much smaller seed sets when being aware of the rate increase, but also is scalable. In addition, considering DIP or not has a significant difference in seed set selection.