Abstract
Recent online social networks such as Twitter, Facebook, and LinkedIn have hurriedly grown in reputation. The resulting accessibility of a social network data supplies an unparalleled occasion for data analysis and mining researchers to resolve useful and semantic information in a broad range of fields such as social sciences, marketing, management, and security. Still, unprocessed social network data are enormous, noisy, scattered, and susceptible in nature, in which some challenges is faced when applying data mining tools and analyzing tasks in storage, efficiency, accuracy, etc. In addition to that there are many problems related to the data collection and data conversion steps in social network data preparation. We focused on the endeavor for privacy preserving social network conversion which provides method for better protection and identification of privacy for social network users and to maintain the convenience of social network data.
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References
Gupta, P., Bhatnagar, V.: Data preprocessing for dynamic social network analysis. In: Data Mining in Dynamic Social Networks and Fuzzy Systems. IGI Global (2013)
Charu, C.: Aggarwal. Social Network Data Analytics. Springer Publishing Company, Incorporated, USA (2011)
Smith, C.: By the numbers: 220 amazing twitter statistics. WWW page (2014)
Sengstock, C., Gertz, M.: Latent geographic feature extraction from social media. In: Proceedings of the 20th International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2012, pp. 149–158 (2012)
Emarketer. Worldwide social network users: 2013 forecast and comparative estimates. WWW page, June 2013
Tatham, M.: Experian marketing services reveals 27 percent of time spent online is on social networking. WWW page, April 2013
Sofean, M., Smith, M.: A real-time architecture for detection of diseases using social networks: design, implementation and evaluation. In: Proceedings of the 23rd ACM Conference on Hypertext and Social Media, HT 2012, pp. 309–310 (2012)
Sun, X., Wang, H., Li, J., Pei, J.: Publishing anonymous survey rating data. Data Min. Knowl. Discov. 23(3), 379–406 (2011)
Tan, E., Guo, L., Chen, S., Zhang, X., Zhao, Y.: Spammer behavior analysis and detection in user generated content on social networks. In: Proceedings of the IEEE 32nd International Conference on Distributed Computing Systems, ICDCS 2012, pp. 305–314 (2012)
Wang, Y., Wu, X., Wu, L.: Differential privacy preserving spectral graph analysis. In: Pei, Jian, Tseng, Vincent S., Cao, Longbing, Motoda, Hiroshi, Xu, Guandong (eds.) PAKDD 2013. LNCS (LNAI), vol. 7819, pp. 329–340. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37456-2_28
Wondracek, G., Holz, T., Kirda, E., Kruegel, C.: A practical attack to de-anonymize social network users. In: Proceedings of the 2010 IEEE Symposium on Security and Privacy, SP 2010, pp. 223–238 (2010)
Xiao, Q., Chen, R., Tan, K.L.: Differentially private network data release via structural inference. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, pp. 911–920 (2014)
Yue, Wang, Xintao, Wu: Preserving differential privacy in degree correlation based graph generation. ACM Trans. Data Privacy 6, 127–145 (2013)
Zhao, W.X., Jiang, J., Weng, J., He, J., Lim, E.P., Yan, H., Li, X.: Comparing twitter and traditional media using topic models. In: Proceedings of the 33rd European Conference on Advances in Information Retrieval, ECIR 2011, pp. 338–349 (2011)
Chen, S., Zhou, S.: Recursive mechanism: towards node differential privacy and unrestricted joins. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, SIGMOD 2013, pp. 653–664 (2013)
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Bazeer Ahamed, B., Yuvaraj, D. (2019). Framework for Faction of Data in Social Network Using Link Based Mining Process. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing & Optimization. ICO 2018. Advances in Intelligent Systems and Computing, vol 866. Springer, Cham. https://doi.org/10.1007/978-3-030-00979-3_31
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DOI: https://doi.org/10.1007/978-3-030-00979-3_31
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