Efficient Influence Maximization in Weighted Independent Cascade Model
Influence maximization (IM) problem which aims to find the most influential seed set in a social network plays an important role in viral marketing. However, previous solutions pay all attention to the structure of network, which causes trouble in real-word applications.
D. Kempe et al.  presented that a non-negative weight can be attached to each node to extend the applicability of traditional models. Although this idea is much applicable in practice, there is little research based on this opinion. Thus, we develop substantial study about this issue. We extend the Independent Cascade (IC) model and present Weighted IC (WIC) model. The IM problem in WIC model is NP-hard. To solve this problem, we present a basic greedy algorithm and Weight Reset (WR) algorithm. Moreover, we propose Bounded WR (BWR) algorithm, a Fully Polynomial-Time Approximation Scheme (FPTAS).
Experimentally, WIC model outperforms IC model in nearly \(90\,\%\) in weighted IM problem. Moreover, BWR achieves excellent approximation and efficiency which is faster than greedy algorithm more than four orders of magnitude. Especially, BWR can handle huge networks with millions of nodes in several tens of seconds while keeping high accuracy. This result demonstrates the effectiveness and efficiency of BWR.
KeywordsGreedy Algorithm Node Selection Giant Component Influence Maximization Independent Cascade
This paper was supported by NGFR 973 grant 2012CB316200, NSFC grant U1509216,61472099,61133002 and National Sci-Tech Support Plan 2015BAH10F01.
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