RESOLVING MULTI-PARTY PRIVACY CONFLICTS IN SOCIAL MEDIA
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The document discusses a new problem in k-nearest neighbor (kNN) search on road networks, incorporating social influence to enhance query results for real-world applications like social networking. It proposes three indexing algorithms—road network-based, social network-based, and a hybrid approach—to efficiently compute social influences in large datasets. Experiments validate that these algorithms are scalable and robust, with suggestions for future research directions.
![CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
CONCLUSION
In this paper, we have studied a new problem: kNN search on road-social
networks (RSkNN). To achieve high efficiency, we first propose a road network-
based indexing algorithm. In this algorithm, we employ a filteringand-
verification framework to answer the RSkNN query. Next, to improve the query
performance, we design social network-based and hybrid indexing algorithms,
namely ISN and IH. Our most efficient algorithm relies on the hybrid index, IH,
that provides tight bounds for the road-social search space. Experiments on
actual road-social networks demonstrate that our solutions are highly scalable
and robust. A direction for future work is to use the techniques in [20] to speed
up query. Another future work is joint social and road processing on networks
stored in a distributed manner. .
REFERENCES
[1] R. K. Ahuja. Network flows. PhD thesis, TECHNISCHE HOCHSCHULE
DARMSTADT, 1993.
[2] D. Angluin and L. G. Valiant. Fast probabilistic algorithms for hamiltonian
circuits and matchings. In Proc. of STOC, 1977.
[3] N. Armenatzoglou, S. Papadopoulos, and D. Papadias. A general framework
for geo-social query processing. PVLDB, 6(10), 2013.
[4] W. Chen, C. Wang, and Y. Wang. Scalable influence maximization for
prevalent viral marketing in large-scale social networks. In KDD, pages 1029–
1038, 2010.](https://image.slidesharecdn.com/rsknnknnsearchonroadnetworksbyincorporatingsocialinfluenced-160722073059/85/RESOLVING-MULTI-PARTY-PRIVACY-CONFLICTS-IN-SOCIAL-MEDIA-2-320.jpg)
![CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
[5] W. Chen, Y. Yuan, and L. Zhang. Scalable influence maximization in social
networks under the linear threshold model. In ICDM, 2010.
[6] D. H. (ed.). Approximation algorithms for NP-Hard problems. PWS, 1997.
[7] U. Feige, M. Hajiaghayi, and J. R. Lee. Improved approximation algorithms
for minimum weight vertex separators. SIAM Journal on Computing,
38(2):629–657, 2008.
[8] G. Karypis and V. Kumar. Analysis of multilevel graph partitioning. In
Supercomputing, page 29, 1995.
[9] D. Kempe, J. Kleinberg, and ´ E. Tardos. Maximizing the spread of influence
through a social network. In KDD, pages 137–146, 2003.
[10] M. Kimura and K. Saito. Tractable models for information diffusion in
social networks. In PKDD, pages 259–271, 2006.
[11] K. Krishna and M. N. Murty. Genetic k-means algorithm. TSMC, 29(3):433–
439, 1999.](https://image.slidesharecdn.com/rsknnknnsearchonroadnetworksbyincorporatingsocialinfluenced-160722073059/85/RESOLVING-MULTI-PARTY-PRIVACY-CONFLICTS-IN-SOCIAL-MEDIA-3-320.jpg)
RESOLVING MULTI-PARTY PRIVACY CONFLICTS IN SOCIAL MEDIA
- 1. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249) MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com RSKNN: KNN SEARCH ON ROAD NETWORKS BY INCORPORATING SOCIAL INFLUENCE ABSTRACT Although kNN search on a road network Gr, i.e., finding k nearest objects to a query user q on Gr, has been extensively studied, existing works neglected the fact that the q’s social information can play an important role in this kNN query. Many real-world applications, such as location-based social networking services, require such a query. In this paper we study a new problem: kNN search on road networks by incorporating social influence (RSkNN). Specifically, the state-of-the-art Independent Cascade (IC) model in social network is applied to define social influence. One critical challenge of the problem is to speed up the computation of the social influence over large road and social networks. To address this challenge, we propose three efficient index-based search algorithms, i.e., road network-based (RN-based), social network-based (SN-based) and hybrid indexing algorithms. In the RN-based algorithm, we employ a filtering-and-verification framework for tackling the hard problem of computing social influence. In the SN-based algorithm, we embed social cuts into the index, so that we speed up the query. In the hybrid algorithm, we propose an index, summarizing the road and social networks, based on which we can obtain query answers efficiently. Finally, we use real road and social network data to empirically verify the efficiency and efficacy of our solutions.
- 2. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249) MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com CONCLUSION In this paper, we have studied a new problem: kNN search on road-social networks (RSkNN). To achieve high efficiency, we first propose a road network- based indexing algorithm. In this algorithm, we employ a filteringand- verification framework to answer the RSkNN query. Next, to improve the query performance, we design social network-based and hybrid indexing algorithms, namely ISN and IH. Our most efficient algorithm relies on the hybrid index, IH, that provides tight bounds for the road-social search space. Experiments on actual road-social networks demonstrate that our solutions are highly scalable and robust. A direction for future work is to use the techniques in [20] to speed up query. Another future work is joint social and road processing on networks stored in a distributed manner. . REFERENCES [1] R. K. Ahuja. Network flows. PhD thesis, TECHNISCHE HOCHSCHULE DARMSTADT, 1993. [2] D. Angluin and L. G. Valiant. Fast probabilistic algorithms for hamiltonian circuits and matchings. In Proc. of STOC, 1977. [3] N. Armenatzoglou, S. Papadopoulos, and D. Papadias. A general framework for geo-social query processing. PVLDB, 6(10), 2013. [4] W. Chen, C. Wang, and Y. Wang. Scalable influence maximization for prevalent viral marketing in large-scale social networks. In KDD, pages 1029– 1038, 2010.
- 3. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249) MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com [5] W. Chen, Y. Yuan, and L. Zhang. Scalable influence maximization in social networks under the linear threshold model. In ICDM, 2010. [6] D. H. (ed.). Approximation algorithms for NP-Hard problems. PWS, 1997. [7] U. Feige, M. Hajiaghayi, and J. R. Lee. Improved approximation algorithms for minimum weight vertex separators. SIAM Journal on Computing, 38(2):629–657, 2008. [8] G. Karypis and V. Kumar. Analysis of multilevel graph partitioning. In Supercomputing, page 29, 1995. [9] D. Kempe, J. Kleinberg, and ´ E. Tardos. Maximizing the spread of influence through a social network. In KDD, pages 137–146, 2003. [10] M. Kimura and K. Saito. Tractable models for information diffusion in social networks. In PKDD, pages 259–271, 2006. [11] K. Krishna and M. N. Murty. Genetic k-means algorithm. TSMC, 29(3):433– 439, 1999.