Maximizing Time-discounted Influential Sustainability in Social Networks
Zeng, Shuai1,2; Ni, Xiaochun1,2; Li, Juanjuan1,2; Yuan, Yong1,2; Wang, Shuai1,3
Conference NameIEEE SMC Workshop on Social Computing and Social Intelligence
Conference DateOct. 5-8, 2017
Conference PlaceBanff, Canada
n social marketing practice, it is usually important to anticipate the long-term impact of the target application to maintain a long-lasting marketing effect, whereas a new product or technology should spread as quickly as possible to establish a competitive advantage.
To find a balance between them, we tackle this challenge by modelling the problem as an issue of time-discounted influential sustainability. Given a threshold µ, the goal of the problem is finding a small subset of nodes as seeds and deciding the optimal timing to activate each seed that could maximize the time-discounted number of iterations, each of which actives more than µ nodes.
 We prove that solving the problem is NP-hard and the objective function is non-negative, non-monotonic, and non-submodular. Therefore we propose a greedy approach to approximately solve this problem. Our experimental results demonstrate that our solution outperforms two baseline algorithms. In order to provide meaningful advices for advertisers on selecting proper initial seed users, we further analyze and compare the performance of four seeding strategies on three typical types of social networks. 
KeywordSocial Marketing Influence Sustainability Information Diffusion Seeding Strategy
Indexed ByEI
Document Type会议论文
Corresponding AuthorYuan, Yong
Affiliation1.The State Key Lab of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences
2.Qingdao Academy of Intelligent Industries
3.University of Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zeng, Shuai,Ni, Xiaochun,Li, Juanjuan,et al. Maximizing Time-discounted Influential Sustainability in Social Networks[C],2017.
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