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 -