Overexposure-aware influence maximization

Loukides, Grigorios, Gwadera, Robert and Chang, Shing-Wan ORCID: https://orcid.org/0000-0002-1587-9076 (2020) Overexposure-aware influence maximization. ACM Transactions On Internet Technology . ISSN 1533-5399 (Accepted/In press)

[img] PDF - Final accepted version (with author's formatting)
Restricted to Repository staff and depositor only

Download (1MB)

Abstract

Viral marketing campaigns are often negatively affected by overexposure. Overexposure occurs when users become less likely to favor a promoted product, after receiving information about the product from too large a fraction of their friends. Yet, existing influence diffusion models do not take overexposure into account, effectively overestimating the number of users who favor the product and diffuse information about it. In this work, we propose the first influence diffusion model that captures overexposure. In our model, LAICO (Latency Aware Independent Cascade Model with Overexposure), the activation probability of a node representing a user is multiplied (discounted) by an overexposure score, which is calculated based on the ratio between the estimated and the maximum possible number of attempts performed to activate the node. We also study the influence maximization problem under LAICO. Since the spread function in LAICO is non-submodular, algorithms for submodular maximization are not appropriate to address the problem. Therefore, we develop an approximation algorithm which exploits monotone submodular upper and lower bound functions of spread, and a heuristic which aims to maximize a proxy function of spread iteratively. Our experiments show the effectiveness and efficiency of our algorithms.

Item Type: Article
Research Areas: A. > Business School > Marketing, Branding and Tourism
Item ID: 30450
Useful Links:
Depositing User: Shing-Wan Chang
Date Deposited: 22 Jun 2020 08:31
Last Modified: 13 Aug 2020 10:54
URI: https://eprints.mdx.ac.uk/id/eprint/30450

Actions (login required)

Edit Item Edit Item

Full text downloads (NB count will be zero if no full text documents are attached to the record)

Downloads per month over the past year