Reachability-Aware Fair Influence Maximization

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Abstract

How can we ensure that an information dissemination campaign reaches every corner of society and also achieves high overall reach? The problem of maximizing the spread of influence over a social network has commonly been considered with an aggregate objective. Less attention has been paid to achieving equality of opportunity, reducing information barriers, and ensuring that everyone in the network has a fair chance to be reached. To that end, the fairness objective aims to maximize the minimum probability of reaching an individual. To address this inapproximable problem, past research has proposed heuristics, which, however, perform less well when the promotion budget is low and achieve fairness at the expense of overall welfare. In this paper, we propose novel reachability-aware algorithms for the fairness-oriented IM problem. Our experimental study shows that our algorithms outperform past work in challenging real-world problem instances by up to a factor of~4 in terms of the fairness objective and strike a balance between fairness and total welfare, even while no solution is universally superior across data, influence probability models, and propagation models.
Original languageEnglish
Title of host publicationWeb and Big Data - 8th International Joint Conference, APWeb-WAIM 2024, Proceedings : 8th International Joint Conference, APWeb-WAIM 2024, Jinhua, China, August 30 – September 1, 2024, Proceedings, Part III
EditorsWenjie Zhang, Zhengyi Yang, Xiaoyang Wang, Anthony Tung, Zhonglong Zheng, Hongjie Guo
Number of pages18
Place of publicationSingapore
PublisherBMJ, Springer Nature
Publication date28 Aug 2024
Pages342-359
ISBN (Print)978-981-97-7237-7
ISBN (Electronic)978-981-97-7238-4
DOIs
Publication statusPublished - 28 Aug 2024
SeriesLecture Notes in Computer Science
Volume14963
ISSN0302-9743

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