TY - JOUR
T1 - Influence Maximization Through Scheduled Seeding in a Real-World Setting
AU - Lev, Tomer
AU - Ben-Gal, Irad
AU - Shmueli, Erez
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2022/4/1
Y1 - 2022/4/1
N2 - In this article, we evaluate, for the first time, the potential of a scheduled seeding strategy for influence maximization in a real-world setting. We first propose methods for analyzing historical data to quantify the infection probability of a node with a given set of properties in a given time and assess the potential of a given seeding strategy to infect nodes. Then, we examine the potential of a scheduled seeding strategy by analyzing a real-world large-scale dataset containing both the network topology as well as the nodes' infection times. Specifically, we use the proposed methods to demonstrate the existence of two important effects in our dataset: a complex contagion effect and a diminishing social influence effect. As shown in a recent study, the scheduled seeding approach is expected to benefit greatly from the existence of these two effects. Finally, we compare a number of benchmark seeding strategies to a scheduled seeding strategy that ranks nodes based on a combination of the number of infectious friends (NIF) they have, as well as the time that has passed since they became infectious. Results of our analyses show that for a seeding budget of 1%, the scheduled seeding strategy yields a convergence rate that is 14% better than a seeding strategy based solely on their degrees, and 215% better than a random seeding strategy, which is often used in practice.
AB - In this article, we evaluate, for the first time, the potential of a scheduled seeding strategy for influence maximization in a real-world setting. We first propose methods for analyzing historical data to quantify the infection probability of a node with a given set of properties in a given time and assess the potential of a given seeding strategy to infect nodes. Then, we examine the potential of a scheduled seeding strategy by analyzing a real-world large-scale dataset containing both the network topology as well as the nodes' infection times. Specifically, we use the proposed methods to demonstrate the existence of two important effects in our dataset: a complex contagion effect and a diminishing social influence effect. As shown in a recent study, the scheduled seeding approach is expected to benefit greatly from the existence of these two effects. Finally, we compare a number of benchmark seeding strategies to a scheduled seeding strategy that ranks nodes based on a combination of the number of infectious friends (NIF) they have, as well as the time that has passed since they became infectious. Results of our analyses show that for a seeding budget of 1%, the scheduled seeding strategy yields a convergence rate that is 14% better than a seeding strategy based solely on their degrees, and 215% better than a random seeding strategy, which is often used in practice.
KW - Influence maximization
KW - scheduled seeding
KW - social network analysis
UR - http://www.scopus.com/inward/record.url?scp=85114744375&partnerID=8YFLogxK
U2 - 10.1109/TCSS.2021.3109043
DO - 10.1109/TCSS.2021.3109043
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AN - SCOPUS:85114744375
SN - 2329-924X
VL - 9
SP - 494
EP - 507
JO - IEEE Transactions on Computational Social Systems
JF - IEEE Transactions on Computational Social Systems
IS - 2
ER -