Maximizing the Influence Ranking Under Limited Cost in Social Network
Influence maximizing is an important problem which has been studied widely in recent years.There are many situations in which people are more concerned about influence ranking than influence coverage in competition network,because some times only the top-ranked users can win the rewards,while few researchers have studied this problem.In this paper,we consider the problem of selecting a seed set under limited cost to get as high influence ranking as possible.We show this problem is NP-hard and propose a Intelligent Greedy algorithm to approximately solve the problem and improve the efficiency based on the submodularity.Furthermore,a new Cost-Effective Multi-Step Influence Adjust algorithm is proposed to get high efficiency.Experimental results show that our Intelligent Greedy algorithm achieves better effectiveness than other algorithms and the Cost-Effective Multi-Step Influence Adjust algorithm achieves high efficiency and gets better effectiveness than Degree and Random algorithms.
Social network Influence propagation Competition Cost Multi-step influence adjust
Xiaoguang Hong Ziyan Liu Zhaohui Peng Zhiyong Chen Hui Li
School of Computer Science and Technology,Shandong University,Jinan,China School of Computer Science and Technology,Shandong University,Jinan,China;National Engineering Labor
国际会议
International Asia-Pacific Web Conference(第18届国际亚太互联网大会)
苏州
英文
220-231
2016-09-23（万方平台首次上网日期，不代表论文的发表时间）