影响最大化问题是在社交网络上找到一组有影响力的用户,使得期望的影响范围最大化。然而,已有的研究工作没有考虑用户之间有效的传播时间区间,而且忽略了营销时间对于选取初始用户的影响。基于真实用户动作日志,确定了用户之间有效的传播时间区间,并提出了一个基于时间的影响力分配模型(influence power allocating model based on time,IPAT)。根据该模型,提出了基于真实时间的影响力最大化问题(influence maximization problem based on time,IMPT)和饥饿营销模式中种集最小化问题(seed set minimization problem in hungry marketing,SMPHM),并证明了这两个问题都是NP-hard问题。为求解IMPT问题和SMPHM问题,分别提出了有效的近似算法A-IMPT(algorithm for IMPT)和A-SMPIB4(algorithm for SMPHM),并证明了算法A-IMPT和A-SMPHM的近似比。多个真实社交网络数据集上的实验验证了算法AMT和A-SMPHM的有效性和高效率。
【英文摘要】
Influence maximization is the problem of finding a small set of influential users in a social network that maximizes the expected spread.However,the existing works often ignore the effective propagation time interval between uses,and also don't take influence of the marketing time on the initial seed users into consideration.This paper uses real action log to determine the effective propagation time interval between users,and proposes an influence power allocating model based on time,called IPAT.According t...
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