Fast growing social networks have been integrated into people's daily lives and play an important role,which makes more and more academics study the social networks from different perspectives.The research of information diffusion over online social networks can help users to obtain information,enterprises to promote product,politicians to regulate public opinion,and is with the significant value in theory and application.In the recent years,although there have been a number of significant advancements on information diffusion,most of them have been mainly focusing on optimization algorithm for user extraction,or evolution equations for behavior law.No attempts have been made to quantify user susceptibility through the forwarding action,and dynamically extract susceptible users.In fact,at different times,the influence of users on information diffusion cannot be exactly the same due to the uncertainty and complexity of social networks.In this sense,dynamic analysis and study of user forwarding action is of great importance,and is precisely what we do in this paper.To address dynamically the business information diffusion problem over online social networks,the randomness and uncertainties of user forwarding action are first analyzed,and then a novel dynamic extraction model for the susceptible users is presented,which is based on Universal Generating Function(UGF) method and Discrete StressStrength Interference (DSSI) theory.In the model,the random forwarding action of the user is firstly quantified as Node Susceptibility (NS),and NS is relevant to two random variables of information receiving Xnmt and forwarding Ynmt.Then,according to UGF method and DSSI theory,the values of NS in regard to different kinds of information at different periods are obtained by deriving the probability distributions of Xnmt and Y and UGFs of Xnmt and Ynmt Finally,the susceptible users are extracted based on dynamic order of the values of NS.The decision results of the model can effectively address the following three issues:(1) the most susceptible users;(2) the kinds of information that they are most susceptible to;and (3) the period when they are most susceptible.The answers to these three questions can provide theoretical basis for making effective strategy of information diffusion,and the decision results can be updated dynamically with the observation parameters.A case study of online group buying website illustrates the feasibility and practicality of the proposed model.Further,based on the same experimental data set,the proposed model is compared with Influence-Susceptibility-Cynical (ISC) model in literature,and different susceptible users are extracted based on ISC model and our model.The results show that the susceptible users extracted in these two models are roughly the same,and the consistency is more than 70 percent.The consistency also illustrates the validity of our model to some extent.On the other hand,the difference between user extractions in different models is analyzed from both theoretical and practical perspectives.Since the quantification of user susceptibility in our model is based on the statistics characteristics of observation parameters,it is concluded that our model is more scientific and reasonable in quantifying user susceptibility.%在线社交网络信息扩散研究有助于用户获取信息、企业推广产品、政府调控舆情,其理论及应用价值巨大.该文主要针对在线社交网络中商业信息的扩散问题,在分析用户转发行为随机性和不确定性的基础上,利用通用发生函数(Universal Generating Function,UGF)方法及离散压力-应力(Discrete Stress Strength Interference,DSSI)理论,提出了一种新颖的信息易感用户动态提取模型.模型中,首先将用户的随机转发行为量化为节点易感性(Node Susceptibility,NS),然后基于UGF方法及DSSI理论计算出不同用户不同时期关于不同信息的NS值,最后通过NS值的动态排序提取信息易感用户.模型的决策结果有效地解决了以下三个问题:(1)哪些用户最易受信息影响？(2)他们最易受哪类信息影响？(3)他们何时最易受影响？这三个问题的答案可为有效信息扩散策略的制定提供理论依据.案例分析和模型对比说明了该模型的可行性及有效性.
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