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Erschienen in: World Wide Web 2/2020

21.12.2019

Three-hop velocity attenuation propagation model for influence maximization in social networks

verfasst von: Weimin Li, Yuting Fan, Jun Mo, Wei Liu, Can Wang, Minjun Xin, Qun Jin

Erschienen in: World Wide Web | Ausgabe 2/2020

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Abstract

In the study of influence maximization in social networks, the speed of information dissemination decreases with increasing time and distance. The investigation of the characteristics of information dissemination is of great significance to the management and control of public opinion. A three-hop velocity decay propagation model is proposed to determine the propagation speed in information dissemination and the time and distance attenuation factors of information dissemination were modeled. We simulated the three-hop information propagation and developed an influence maximization algorithm based on the rate attenuation propagation model (IMMRA). Experiments using two example data sets showed that the proposed algorithm had higher accuracy and time efficiency than a greedy algorithm.

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Literatur
1.
Zurück zum Zitat Kempe D, Kleinberg J, Tardos E. Maximizing the spread of influence in a social network .Proceeding s of the 9th AC M SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington, 2003:137–146 Kempe D, Kleinberg J, Tardos E. Maximizing the spread of influence in a social network .Proceeding s of the 9th AC M SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington, 2003:137–146
2.
Zurück zum Zitat Goyal A,Wei Lu,Lakshmanan L V S.CELF++:optimizing the greedy algorithm for influence maximization in social networks. Proc of the 20th International Conference Companion on World Wide Web, 2011:47–48 Goyal A,Wei Lu,Lakshmanan L V S.CELF++:optimizing the greedy algorithm for influence maximization in social networks. Proc of the 20th International Conference Companion on World Wide Web, 2011:47–48
3.
Zurück zum Zitat Wei Chen,Yajun Wang,Siyu Yang. Efficient Influence Maximization in Social Networks. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Paris, France, 2009:199–207 Wei Chen,Yajun Wang,Siyu Yang. Efficient Influence Maximization in Social Networks. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Paris, France, 2009:199–207
4.
Zurück zum Zitat Chen, H., Wang, Y.: Threshold-based heuristic algorithm for influence maximization. Journal of Computer Research and Development. 49(10), 2181–2188 (2012) Chen, H., Wang, Y.: Threshold-based heuristic algorithm for influence maximization. Journal of Computer Research and Development. 49(10), 2181–2188 (2012)
5.
Zurück zum Zitat Wang, J., Wang, Y., Feng, X.: A new hybrid algorithm for influence maximization in social networks. Chinese Journal of Computer. 34(10), 1956–1965 (2011)MathSciNetCrossRef Wang, J., Wang, Y., Feng, X.: A new hybrid algorithm for influence maximization in social networks. Chinese Journal of Computer. 34(10), 1956–1965 (2011)MathSciNetCrossRef
6.
Zurück zum Zitat Kyomin Jung,Wooram Heo,Wei Chen.IRIE: Scalable and Robust Influence Maximization in Social Networks. Data Mining(ICDM),2012 IEEE 12th International Conference on.2012:918–923, ISSN 1550-4786 Kyomin Jung,Wooram Heo,Wei Chen.IRIE: Scalable and Robust Influence Maximization in Social Networks. Data Mining(ICDM),2012 IEEE 12th International Conference on.2012:918–923, ISSN 1550-4786
7.
Zurück zum Zitat Wang, W., Street, W.N.: Modeling and maximizing influence diffusion in social networks for viral marketing. Applied network science. 3(1), 6 (2018)CrossRef Wang, W., Street, W.N.: Modeling and maximizing influence diffusion in social networks for viral marketing. Applied network science. 3(1), 6 (2018)CrossRef
8.
Zurück zum Zitat Kempe D, Kleinberg J, Tardos E. Maximizing the spread of influence in a social network .Proceeding s of the 9th AC M SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington, 2003:137–146 Kempe D, Kleinberg J, Tardos E. Maximizing the spread of influence in a social network .Proceeding s of the 9th AC M SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington, 2003:137–146
9.
Zurück zum Zitat Song, G., Li, Y., Chen, X., He, X., Tang, J.: Influential node tracking on dynamic social network: an interchange greedy approach. IEEE Transactions on Knowledge & Data Engineering. 99, 1–1 (2017) Song, G., Li, Y., Chen, X., He, X., Tang, J.: Influential node tracking on dynamic social network: an interchange greedy approach. IEEE Transactions on Knowledge & Data Engineering. 99, 1–1 (2017)
10.
Zurück zum Zitat Liu, B., Gao, C., Zeng, Y., Xu, D., Chee, Y.M.: Influence spreading path and its application to the time constrained social influence maximization problem and beyond. IEEE Transactions on Automation Science & Engineering. 26(8), 1904–1917 (2014) Liu, B., Gao, C., Zeng, Y., Xu, D., Chee, Y.M.: Influence spreading path and its application to the time constrained social influence maximization problem and beyond. IEEE Transactions on Automation Science & Engineering. 26(8), 1904–1917 (2014)
11.
Zurück zum Zitat Nguyen, H., Zheng, R.: On budgeted influence maximization in social networks. IEEE Journal on Selected Areas in Communications. 31(6), 1084–1094 (2013)CrossRef Nguyen, H., Zheng, R.: On budgeted influence maximization in social networks. IEEE Journal on Selected Areas in Communications. 31(6), 1084–1094 (2013)CrossRef
12.
Zurück zum Zitat Lee J R , Chung C W . A Fast Approximation for Influence Maximization in Large Social Networks [J]. 2014CrossRef Lee J R , Chung C W . A Fast Approximation for Influence Maximization in Large Social Networks [J]. 2014CrossRef
13.
Zurück zum Zitat Gong, M., Song, C., Duan, C., et al.: An efficient Memetic algorithm for influence maximization in social networks [J]. IEEE Comput. Intell. Mag. 11(3), 22–33 (2016)CrossRef Gong, M., Song, C., Duan, C., et al.: An efficient Memetic algorithm for influence maximization in social networks [J]. IEEE Comput. Intell. Mag. 11(3), 22–33 (2016)CrossRef
14.
Zurück zum Zitat Zheng H , Wu J . Friend Recommendation in Online Social Networks: Perspective of Social Influence Maximization[C]// 2017 26th International Conference on Computer Communication and Networks (ICCCN). IEEE, 2017 Zheng H , Wu J . Friend Recommendation in Online Social Networks: Perspective of Social Influence Maximization[C]// 2017 26th International Conference on Computer Communication and Networks (ICCCN). IEEE, 2017
15.
Zurück zum Zitat J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. VanBriesen, and N. S. Glance, “Cost-effective outbreak detection in networks.” in KDD, 2007, pp. 420–429 J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. VanBriesen, and N. S. Glance, “Cost-effective outbreak detection in networks.” in KDD, 2007, pp. 420–429
16.
Zurück zum Zitat Zhou, C., Zhang, P., Guo, J., Zhu, X., Guo, L.: Ublf: an upper bound based approach to discover influential nodes in social networks. In: ICDM (2013) Zhou, C., Zhang, P., Guo, J., Zhu, X., Guo, L.: Ublf: an upper bound based approach to discover influential nodes in social networks. In: ICDM (2013)
17.
Zurück zum Zitat M. G. Rodriguez and B. Scholkopf, “Influence maximization in continuous time diffusion networks,” arXiv preprint arXiv: 1205.1682, 2012 M. G. Rodriguez and B. Scholkopf, “Influence maximization in continuous time diffusion networks,” arXiv preprint arXiv: 1205.1682, 2012
18.
Zurück zum Zitat Chen, W., Lin, T., Tan, Z., Zhao, M., & Zhou, X.. Robust influence maximization. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 795–804). ACM.2016 Chen, W., Lin, T., Tan, Z., Zhao, M., & Zhou, X.. Robust influence maximization. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 795–804). ACM.2016
19.
Zurück zum Zitat Y. Tang, Y. Shi, and X. Xiao, “Influence maximization in near linear time: a martingale approach,” in Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. ACM, 2015, pp. 1539–1554 Y. Tang, Y. Shi, and X. Xiao, “Influence maximization in near linear time: a martingale approach,” in Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. ACM, 2015, pp. 1539–1554
20.
Zurück zum Zitat Shi, Q., Wang, C., Chen, J., et al.: Location driven influence maximization: online spread via offline deployment [J]. Knowl.-Based Syst. 166, 30–41 (2019)CrossRef Shi, Q., Wang, C., Chen, J., et al.: Location driven influence maximization: online spread via offline deployment [J]. Knowl.-Based Syst. 166, 30–41 (2019)CrossRef
21.
Zurück zum Zitat Sun, L., Huang, W., Yu, P.S., et al.: Multi-round influence maximization[C]//proceedings of the 24th ACM SIGKDD international conference on Knowledge Discovery & Data Mining. ACM. 2249–2258 (2018) Sun, L., Huang, W., Yu, P.S., et al.: Multi-round influence maximization[C]//proceedings of the 24th ACM SIGKDD international conference on Knowledge Discovery & Data Mining. ACM. 2249–2258 (2018)
22.
Zurück zum Zitat Barabási, A.-L.: The origin of bursts and heavy tails in human dynamics [J]. Nature. (2005) Barabási, A.-L.: The origin of bursts and heavy tails in human dynamics [J]. Nature. (2005)
23.
Zurück zum Zitat Vazquez A,Oliveira J G,DezseZ,et al. Modeling burst and heavy tails in human dynamics[J]. Physical Review E,2006,73(3):036127 Vazquez A,Oliveira J G,DezseZ,et al. Modeling burst and heavy tails in human dynamics[J]. Physical Review E,2006,73(3):036127
Metadaten
Titel
Three-hop velocity attenuation propagation model for influence maximization in social networks
verfasst von
Weimin Li
Yuting Fan
Jun Mo
Wei Liu
Can Wang
Minjun Xin
Qun Jin
Publikationsdatum
21.12.2019
Verlag
Springer US
Erschienen in
World Wide Web / Ausgabe 2/2020
Print ISSN: 1386-145X
Elektronische ISSN: 1573-1413
DOI
https://doi.org/10.1007/s11280-019-00750-5

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