Proceedings Article10.1109/ICTAI.2013.129
CPP-SNS: A Solution to Influence Maximization Problem under Cost Control
Qianyi Zhan,Hongchao Yang,Chongjun Wang,Junyuan Xie +3 more
- 04 Nov 2013
- pp 849-856
3
TL;DR: A new algorithm called CPP-SNS is proposed, which selects seeds according to cost performance of nodes, and extensive experiments show this method has a good performance in different social networks.
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Abstract: As more and more people join social network, viral marketing on online social network becomes a new trend of advertising. Motivated by this, plenty of research focuseson how to maximize the information propagation, which is called the influence maximization problem. Traditional work has made significant progress on this topic. However all ad companies have marketing budget, the research of influence maximization problem should take account of cost control. Under the condition of cost control, we model each user's cost of helping spread information as a feature of each node in the network. Then we modify several most widely studied algorithms to suit the new model. In this paper, a new algorithm called CPP-SNS is proposed, which selects seeds according to cost performance of nodes. Further improvements, based on strategy of partial node loading and submodular property of spread function, make CPP-SNS more effective in practical scenarios. Extensive experiments show this method has a good performance in different social networks. Based on results of our research, we also provide some advice for the practical marketing.
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Citations
Viral Marketing for Digital Goods in Social Networks
Yu Qiao,Jun Wu,Lei Zhang,Chongjun Wang +3 more
- 07 Jul 2017
TL;DR: This paper considers how to sell the digital goods by viral marketing in social network by adopting two efficient algorithms from two approaches, including a famous algorithm from theoretical computer science that can achieve a tight linear time approximation.
1
Budget-aware Influence Maximization in Social Networks
ZUO Yuan-lin,GONG Yue-jiao,CHEN Wei-neng +2 more
TL;DR: This paper proposes a budget-aware influence maximization model and algorithm, CDACS, to select source nodes in social networks under cost constraints, achieving a 15% improvement in coverage rate and 20% reduction in running time overhead compared to traditional ant colony algorithms.
Fast influence maximization algorithm in social network under budget control
Yuanying LIU,Jingfeng GUO,Lidong WEI,Xinzhuan HU +3 more
TL;DR: A fast influence maximization algorithm, BCIM, is proposed to optimize influence scope under budget control, outperforming greedy algorithms in seed set quality, but with higher running time compared to random and greedy algorithms.
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David Kempe,Jon Kleinberg,Éva Tardos +2 more
- 24 Aug 2003
TL;DR: An analysis framework based on submodular functions shows that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models, and suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.
Maximizing the Spread of Influence through a Social Network
TL;DR: The problem of finding the most influential nodes in a social network is NP-hard as mentioned in this paper, and the first provable approximation guarantees for efficient algorithms were provided by Domingos et al. using an analysis framework based on submodular functions.
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Pedro Domingos,Matthew Richardson +1 more
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TL;DR: It is proposed to model also the customer's network value: the expected profit from sales to other customers she may influence to buy, the customers those may influence, and so on recursively, taking advantage of the availability of large relevant databases.
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Cost-effective outbreak detection in networks
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TL;DR: This work exploits submodularity to develop an efficient algorithm that scales to large problems, achieving near optimal placements, while being 700 times faster than a simple greedy algorithm and achieving speedups and savings in storage of several orders of magnitude.
Efficient influence maximization in social networks
Wei Chen,Yajun Wang,Siyu Yang +2 more
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TL;DR: Based on the results, it is believed that fine-tuned heuristics may provide truly scalable solutions to the influence maximization problem with satisfying influence spread and blazingly fast running time.