Reachability-aware fair influence maximization

Author

Wenyue MA, Aarhus University
Maximilian K. EGGER, Aarhus University
Andreas PAVLOGIANNIS, Aarhus University
Yuchen LI, Singapore Management UniversityFollow
Panagiotis KARRAS, Aarhus University

Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

8-2024

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.

Keywords

Reachability-aware algorithm, Information dissemination

Discipline

Communication Technology and New Media | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Web and Big Data: 8th International Joint Conference, APWeb-WAIM 2024: Jinhua, China, August 30 – September 1: Proceedings

Volume

14963

First Page

342

Last Page

359

ISBN

9789819772384

Identifier

10.1007/978-981-97-7238-4_22

Publisher

Springer

City or Country

Cham

Citation

MA, Wenyue; EGGER, Maximilian K.; PAVLOGIANNIS, Andreas; LI, Yuchen; and KARRAS, Panagiotis. Reachability-aware fair influence maximization. (2024). Web and Big Data: 8th International Joint Conference, APWeb-WAIM 2024: Jinhua, China, August 30 – September 1: Proceedings. 14963, 342-359.
Available at: https://ink.library.smu.edu.sg/sis_research/9723

Copyright Owner and License

Authors

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

Additional URL

https://doi.org/10.1007/978-981-97-7238-4_22