TY - GEN

T1 - Viral vs. Effective

T2 - 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020

AU - Sabato, Yael

AU - Azaria, Amos

AU - Hazon, Noam

N1 - Publisher Copyright:
© 2020 International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). All rights reserved.

PY - 2020

Y1 - 2020

N2 - The computational problem of Influence maximization concerns the selection of an initial set of nodes in a social network such that, by sending this set a certain message, its exposure through the network will be the highest. We propose to study this problem from a utilitarian point of view. That is, we study a model where there are two types of messages; one that is more likely to be propagated but gives a lower utility per user obtaining this message, and another that is less likely to be propagated but gives a higher utility. In our model the utility from a user that receives both messages is not necessarily the sum of the two utilities. The goal is to maximize the overall utility. Using an analysis based on bisubmodular functions, we show a greedy algorithm with a tight approximation ratio of 12. We develop a dynamic programming based algorithm that is more suitable to our setting and show through extensive simulations that it outperforms the greedy algorithm.

AB - The computational problem of Influence maximization concerns the selection of an initial set of nodes in a social network such that, by sending this set a certain message, its exposure through the network will be the highest. We propose to study this problem from a utilitarian point of view. That is, we study a model where there are two types of messages; one that is more likely to be propagated but gives a lower utility per user obtaining this message, and another that is less likely to be propagated but gives a higher utility. In our model the utility from a user that receives both messages is not necessarily the sum of the two utilities. The goal is to maximize the overall utility. Using an analysis based on bisubmodular functions, we show a greedy algorithm with a tight approximation ratio of 12. We develop a dynamic programming based algorithm that is more suitable to our setting and show through extensive simulations that it outperforms the greedy algorithm.

KW - Influence maximization

KW - Social networks

UR - http://www.scopus.com/inward/record.url?scp=85096704141&partnerID=8YFLogxK

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AN - SCOPUS:85096704141

T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS

SP - 1169

EP - 1177

BT - Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020

A2 - An, Bo

A2 - El Fallah Seghrouchni, Amal

A2 - Sukthankar, Gita

Y2 - 9 May 2020 through 13 May 2020

ER -