TY - GEN

T1 - Seed investment bounds for viral marketing under generalized diffusion

AU - Ghayoori, Arash

AU - Nagi, Rakesh

N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2019/8/27

Y1 - 2019/8/27

N2 - This paper attempts to provide viral marketeers guidance in terms of an investment level that could help capture some desired γ percentage of the market-share by some target time t with a desired level of confidence. To do this, we first introduce a diffusion model for social networks. A distance-dependent random graph is then considered as a model for the underlying social network, which we use to analyze the proposed diffusion model. Using the fact that vertices degrees have an almost Poisson distribution in distance-dependent random networks, we then provide a lower bound on the probability of the event that the time it takes for an idea (or a product, disease, etc.) to dominate a pre-specified γ percentage of a social network (denoted by Rγ) is smaller than some pre-selected target time t > 0, i.e., we find a lower bound on the probability of the event {Rγ ≤ t}. Simulation results performed over a wide variety of networks, including random as well as real-world, are then provided to verify that our bound indeed holds in practice. The Kullback-Leibler divergence measure is used to evaluate performance of our lower bound over these groups of networks, and as expected, we note that for networks that deviate more from the Poisson degree distribution, our lower bound does worse.

AB - This paper attempts to provide viral marketeers guidance in terms of an investment level that could help capture some desired γ percentage of the market-share by some target time t with a desired level of confidence. To do this, we first introduce a diffusion model for social networks. A distance-dependent random graph is then considered as a model for the underlying social network, which we use to analyze the proposed diffusion model. Using the fact that vertices degrees have an almost Poisson distribution in distance-dependent random networks, we then provide a lower bound on the probability of the event that the time it takes for an idea (or a product, disease, etc.) to dominate a pre-specified γ percentage of a social network (denoted by Rγ) is smaller than some pre-selected target time t > 0, i.e., we find a lower bound on the probability of the event {Rγ ≤ t}. Simulation results performed over a wide variety of networks, including random as well as real-world, are then provided to verify that our bound indeed holds in practice. The Kullback-Leibler divergence measure is used to evaluate performance of our lower bound over these groups of networks, and as expected, we note that for networks that deviate more from the Poisson degree distribution, our lower bound does worse.

KW - Diffusion models

KW - Influence maximization

KW - Random graph models

KW - Social networks

KW - Viral marketing

UR - http://www.scopus.com/inward/record.url?scp=85078824179&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85078824179&partnerID=8YFLogxK

U2 - 10.1145/3341161.3342922

DO - 10.1145/3341161.3342922

M3 - Conference contribution

AN - SCOPUS:85078824179

T3 - Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019

SP - 95

EP - 100

BT - Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019

A2 - Spezzano, Francesca

A2 - Chen, Wei

A2 - Xiao, Xiaokui

PB - Association for Computing Machinery, Inc

T2 - 11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019

Y2 - 27 August 2019 through 30 August 2019

ER -