Journal
JOURNAL OF COMBINATORIAL OPTIMIZATION
Volume 29, Issue 4, Pages 685-700Publisher
SPRINGER
DOI: 10.1007/s10878-013-9616-x
Keywords
Approximation algorithm; Improving loyalty; Bounded budget; Modular social networks
Funding
- National Natural Science Foundation of China [61070191, 91124001]
- Research Fund for the Doctoral Program of Higher Education of China [20100004110001]
- U.S. National Science Foundation [CNS-0831579, CNS-1016320, CCF-0829993]
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The influence maximization problem in modular social networks is to find a set of seed nodes such that the total influence effect is maximized. Difference with the previous research, in this paper we propose a novel task of influence improving, which is to find strategies to increase the members' investments. The problem is studied under two influence propagation models: independent cascade (IC) and linear threshold (LT) models. We prove that our influence improving problem is -hard, and propose new algorithms under both IC and LT models. To the best of our knowledge, our work is the first one that studies influence improving problem under bounded budget. Finally, we implement extensive experiments over a large data collection obtained from real-world social networks, and evaluate the performance of our approach.
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