Modeling the Spread of Influence for Independent Cascade Diffusion Process in Social Networks
2017 IEEE 37th International Conference onDistributed Computing Systems Workshops (ICDCSW)
Modeling the spread of influence in online social networks is important for predicting the influence of individuals and better understanding many scenarios in social networks, such as the influence maximization problem. The previous work on modeling the spread of influence makes the assumption that the statuses of nodes in a network are independent of each other, which is apparently not correct for social networks. The goal of this work is to derive an accurate mathematical model to characterize the spread of influence for the independent cascade diffusion process in online social networks. Specifically, we apply the susceptible-infected-recovered epidemic model from epidemiology to characterize the independent cascade diffusion process and derive a general mathematical framework. To approximate the complex spatial dependence among nodes in a network, we propose a Markov model to predict the spread of influence. Through the extensive simulation study over several generated topologies and a real coauthorship network, we show that our designed Markov model has much better performance than the existing independent model in predicting the influence of individuals in online social networks.
Social influence, online social networks, independent cascade diffusion, spatial dependence, Markov dependence, influence spread modeling, independent cascade diffusion process, online social networks, influence maximization problem, susceptible-infected-recovered epidemic model, general mathematical framework, complex spatial dependence, Markov model, generated topologies, real coauthorship network, Markov processes, social networking (online), social sciences computing, Mathematical model, Social network services, Diffusion processes, Markov processes, Integrated circuit modeling, Predictive models
Z. Chen and Kurtis Taylor (2017).
Modeling the Spread of Influence for Independent Cascade Diffusion Process in Social Networks. Presented at 2017 IEEE 37th International Conference onDistributed Computing Systems Workshops (ICDCSW), Atlanta, GA.