IMpart: A Partitioning-based Parallel Approach to Accelerate Influence Maximization

Barik, Reet; Minutoli, Marco; Halappanavar, Mahantesh; Kalyanaraman, Ananth

doi:10.1109/hipc56025.2022.00028

Cited by 2 publications

(1 citation statement)

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“…The problem of influence maximization in viral markets has recently attracted the interest of the research community aiming at maximizing the influence spread into the OSNs by strategically selecting a set of influencers [10,11]. The problem of scalability posed by the massive user base of OSNs is studied in [12] by designing distributed algorithms for influence maximization in viral marketing.…”

Section: Influence Maximizationmentioning

confidence: 99%

INFLUTRUST: Trust-Based Influencer Marketing Campaigns in Online Social Networks

Adesokan,

Rahman,

Tsiropoulou

2024

Future Internet

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This paper introduces the INFLUTRUST framework that is designed to address challenges in trust-based influencer marketing campaigns on Online Social Networks (OSNs). The INFLUTRUST framework enables the influencers to autonomously select products across the OSN platforms for advertisement by employing a reinforcement learning algorithm. The Stochastic Learning Automata reinforcement algorithm considers the OSN platforms’ provided monetary rewards, the influencers’ advertising profit, and the influencers’ trust levels towards the OSN platforms to enable the influencers to autonomously select an OSN platform. The trust model for the influencers incorporates direct and indirect trust, which are derived from past interactions and social ties among the influencers and the OSN platforms, respectively. The OSN platforms allocate rewards through a multilateral bargaining model that supports competition among the influencers. Simulation-based results validate the INFLUTRUST framework’s effectiveness across diverse scenarios, with the scalability analysis demonstrating its robustness. Comparative evaluations highlight the INFLUTRUST framework’s superiority in considering trust levels and reward allocation fairness, benefiting both the influencers and the OSN platforms.

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Section: Influence Maximizationmentioning

confidence: 99%