SCIENCE CHINA Information Sciences, Volume 60 , Issue 10 : 108101(2017) https://doi.org/10.1007/s11432-016-9080-3

## Relative influence maximization in competitive social networks

• AcceptedApr 24, 2017
• PublishedAug 9, 2017
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### Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant Nos. 61472400, 61300105), Research Fund for Doctoral Program of Higher Education of China (Grant No. 2012351410010), Key Project of Science and Technology of Fujian (Grant No. 2013H6012), Key Laboratory of Network Data Science & Technology, and Chinese Science and Technology Foundation (Grant No. CASNDST20140X).

### References

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• Figure 1

(Color online) Experimental results. (a) Relative influence of greedy algorithms with different $k$ in NetHEPT; (b) relative influence of greedy algorithms with different $k$ in Geom; (c) relative influence of heuristics with different $k$ in NetHEPT; (d) relative influence of heuristics with different $k$ in Geom; (e) running times with $k$ =100 in NetHEPT and Geom; (f) running times with $k$ =100 in synthetic networks.

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