属性网络中的非子模约束利润最大化

Liman Du, Wenguo Yang, Suixiang Gao
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引用次数: 0

摘要

通过社交网络与朋友互动的社会个体数量正在增加,这导致了一个不可否认的事实,即口碑营销已经成为促进产品销售的有用方法之一。属性网络中的约束利润最大化问题作为经典影响最大化问题的延伸,是本文研究的重点。提出了一种更接近实际情况的基数约束下的属性网络问题的利润最大化问题。CPMA的利润差度量计算所有活动节点产生的总收益和成本。与传统的影响最大化问题不同,影响强度需要根据属性网络中节点的情感倾向和分类标签重新计算。一般而言,利润差度量不再是单调和次模的。考虑到利润差度量可以表示为两个子模函数之差,并允许DS分解,提出了一种基于Louvain算法和logistic函数的基于边际增量和基于社区的剪枝搜索(MCPS)三阶段算法框架。由于采用边际增量的方法,MPCS算法可以更直接、更准确地计算利润差。实验证明了该算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Nonsubmodular Constrained Profit Maximization in Attribute Networks
The number of social individuals who interact with their friends through social networks is increasing, leading to an undeniable fact that word-of-mouth marketing has become one of the useful ways to promote sale of products. The Constrained Profit Maximization in Attribute network (CPMA) problem, as an extension of the classical influence maximization problem, is the main focus of this paper. We propose the profit maximization in attribute network problem under a cardinality constraint which is closer to the actual situation. The profit spread metric of CPMA calculates the total benefit and cost generated by all the active nodes. Different from the classical Influence Maximization problem, the influence strength should be recalculated according to the emotional tendency and classification label of nodes in attribute networks. The profit spread metric is no longer monotone and submodular in general. Given that the profit spread metric can be expressed as the difference between two submodular functions and admits a DS decomposition, a three-phase algorithm named as Marginal increment and Community-based Prune and Search(MCPS) Algorithm frame is proposed which is based on Louvain algorithm and logistic function. Due to the method of marginal increment, MPCS algorithm can compute profit spread more directly and accurately. Experiments demonstrate the effectiveness of MCPS algorithm.
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