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CPP-SNS: A Solution to Influence Maximization Problem under Cost Control

机译：CPP-SNS：一种在成本控制下影响最大化问题的解决方案

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摘要

As more and more people join social network, viral marketing on online social network becomes a new trendof advertising. Motivated by this, plenty of research focuseson how to maximize the information propagation, which iscalled the influence maximization problem. Traditional workhas made significant progress on this topic. However all adcompanies have marketing budget, the research of influencemaximization problem should take account of cost control. Under the condition of cost control, we model each user'scost of helping spread information as a feature of each nodein the network. Then we modify several most widely studiedalgorithms to suit the new model. In this paper, a new algorithmcalled CPP-SNS is proposed, which selects seeds according tocost performance of nodes. Further improvements, based onstrategy of partial node loading and submodular property ofspread function, make CPP-SNS more effective in practicalscenarios. Extensive experiments show this method has a goodperformance in different social networks. Based on results ofour research, we also provide some advice for the practical marketing.
机译：随着越来越多的人加入社交网络，在线社交网络上的病毒式营销成为广告的一种新趋势。因此，大量的研究集中在如何最大化信息传播上，这被称为影响最大化问题。传统工作在该主题上取得了重大进展。但是，所有广告公司都有营销预算，因此，影响最大化问题的研究应考虑成本控制。在成本控制的条件下，我们将帮助传播信息的每个用户的成本建模为网络中每个节点的功能。然后，我们修改一些最广泛研究的算法以适应新模型。本文提出了一种新的算法，称为CPP-SNS，它根据节点的成本性能选择种子。基于部分节点加载的策略和扩展函数的子模属性，进一步的改进使CPP-SNS在实际场景中更加有效。大量实验表明，该方法在不同的社交网络中均具有良好的性能。根据我们的研究结果，我们还为实际营销提供了一些建议。

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