A Greedy Algorithm Approach for Influential Node Tracking on Dynamic Social Network
Keywords:
Influence maximization, Mobile social network, community greedy algorithm, and Location based community greedy algorithm.Abstract
A mobilesocial network assumes a critical part as the spread of data and influence as "informal". It is essential thing to discover little arrangement of powerful individuals in a mobilesocial network with the end goal that focusing on them at first. It will expand the spread of the influence.The issue of finding the most powerful nodes in arranges is NP-hard. It has been demonstrated that a Greedy algorithm with provable estimate certifications can give great guess. Group based Greedy algorithm is utilized for mining top-K persuasive nodes. It has two parts: partitioning the mobilesocial network into a few groups by considering data dissemination and choosing groups to discover powerful nodes by a dynamic programming. Location Based community Greedy algorithm is utilized to discover the influencenode in view of Location and consider the influence spread inside Particular region. Examinations result on genuine expansive scale mobile informal organizations demonstrate that the proposed location based insatiable algorithm has higher effectiveness than past group greedyalgorithm.
References
- JLeskovec,J.Kleinberg,andC.Faloutsos,“Graphsovertime:densificationlaws,shrinking diameters and possible explanations,” in KDD, 2005, pp. 177–187.
- JLeskovec,J.M.Kleinberg, and C. Faloutsos,“Graphevolution: Densificationand shrinking diameters,”TKDD, vol. 1, 2007.
- JLeskovec, L.Backstrom, R. Kumar, and A.Tomkins,“Microscopic evolution of social networks,” in KDD, 2008, pp. 462–470.
- C Zhou, P Zhang, J Guo, X.Zhu, and L.Guo,“Ublf: An upper bound based approach todiscover influential nodes in social networks,” in ICDM, 2013.
- M Richardson and P. Domingos, “Mining knowledge-sharing sites for viral marketing,” in KDD, 2002, pp. 61–70.
- JLeskovec,A.Krause,C.Guestrin,C. Faloutsos, J. VanBriesen, and N. S. Glance,“Cost-effective out break detection in networks.” in KDD, 2007, pp. 420–429.
- Q Jiang, G. Song, G. Cong, Y. Wang, W. Si, and K. Xie, “Simulated annealing based influence maximization in social network,” in AAAI, 2011.
- K Jung, W. Heo, and W. Chen, “Irie: Scalable and robust influence maximization in social networks,” in ICDM, 2012, pp. 918–923.
- M G. Rodriguez and B. Scholkopf, “Influence maximization in continuous time diffusion networks,” arXiv preprint arXiv:1205.1682, 2012
- W. Chen, Y. Wang, and S. Yang, “Efficient influence maximization in social network,” in KDD, 2009, pp. 199–208.
- P. Domingos and M. Richardson, “Mining the network value of customers,” in KDD, 2001, pp. 57–66.
- D. Kempe, J. Kleinberg, and E. Tardos, “Maximizing the spread of inffluence through a social network,” in KDD, 2003, pp. 137–146.
- M. Kimura and K. Saito, “Tractable models for information diffusion in social networks,” in PKDD, 2006, pp. 259–271.
- W.Yu, G.Cong, G.Song, and K.Xie, “Community-based greedy algorithm for mining top-k influential nodes in mobile social networks,” in KDD, 2010, pp. 1039–1048.
- W.Chen, C.Wang, and Y.Wang, “Scalable influence maximization for prevalent viral marketing in large-scale social networks,” in KDD, 2010, pp. 1029–1038.
- W. Chen, W. Lu, and N. Zhang, “Time-criticalinfluence maximization in social networks with time-delayed diffusion process,” in AAAI, 2012
Downloads
Published
Issue
Section
License
Copyright (c) IJSRCSEIT
This work is licensed under a Creative Commons Attribution 4.0 International License.