TY - GEN
T1 - Multi-objective influence maximization
AU - Gershtein, Shay
AU - Milo, Tova
AU - Youngmann, Brit
N1 - Publisher Copyright:
© 2021 Copyright held by the owner/author(s).
PY - 2021
Y1 - 2021
N2 - Influence Maximization (IM) is the problem of finding a set of influential users in a social network, so that their aggregated influence is maximized. The classic IM problem focuses on the single objective of maximizing the overall number of influenced users. While this serves the goal of reaching a large audience, users often have multiple specific sub-populations they would like to reach within a single campaign, and consequently multiple influence maximization objectives. As we show, maximizing the influence over one group may come at the cost of significantly reducing the influence over the others. To address this, we propose IM-Balanced, a system that allows users to explicitly declare the desired balance between the objectives. IM-Balanced employs a refined notion of the classic IM problem, called Multi-Objective IM, where all objectives except one are turned into constraints, and the remaining objective is optimized subject to these constraints. We prove Multi-Objective IM to be harder to approximate than the original IM problem, and correspondingly provide two complementary approximation algorithms, each suiting a different prioritization pertaining to the inherent trade-off between the objectives. In our experiments we compare our solutions both to existing IM algorithms as well as to alternative approaches, demonstrating the advantages of our algorithms.
AB - Influence Maximization (IM) is the problem of finding a set of influential users in a social network, so that their aggregated influence is maximized. The classic IM problem focuses on the single objective of maximizing the overall number of influenced users. While this serves the goal of reaching a large audience, users often have multiple specific sub-populations they would like to reach within a single campaign, and consequently multiple influence maximization objectives. As we show, maximizing the influence over one group may come at the cost of significantly reducing the influence over the others. To address this, we propose IM-Balanced, a system that allows users to explicitly declare the desired balance between the objectives. IM-Balanced employs a refined notion of the classic IM problem, called Multi-Objective IM, where all objectives except one are turned into constraints, and the remaining objective is optimized subject to these constraints. We prove Multi-Objective IM to be harder to approximate than the original IM problem, and correspondingly provide two complementary approximation algorithms, each suiting a different prioritization pertaining to the inherent trade-off between the objectives. In our experiments we compare our solutions both to existing IM algorithms as well as to alternative approaches, demonstrating the advantages of our algorithms.
UR - http://www.scopus.com/inward/record.url?scp=85113717090&partnerID=8YFLogxK
U2 - 10.5441/002/edbt.2021.14
DO - 10.5441/002/edbt.2021.14
M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???
AN - SCOPUS:85113717090
T3 - Advances in Database Technology - EDBT
SP - 145
EP - 156
BT - Advances in Database Technology - EDBT 2021
A2 - Velegrakis, Yannis
A2 - Velegrakis, Yannis
A2 - Zeinalipour, Demetris
A2 - Chrysanthis, Panos K.
A2 - Chrysanthis, Panos K.
A2 - Guerra, Francesco
PB - OpenProceedings.org
T2 - Advances in Database Technology - 24th International Conference on Extending Database Technology, EDBT 2021
Y2 - 23 March 2021 through 26 March 2021
ER -