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.
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
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 -