Download Big data in complex and social networks by My T. Thai, Weili Wu, Hui Xiong PDF

By My T. Thai, Weili Wu, Hui Xiong

This ebook offers contemporary advancements at the theoretical, algorithmic, and alertness facets of huge information in complicated and Social Networks. The ebook contains 4 components, overlaying a variety of themes. the 1st a part of the e-book makes a speciality of info garage and information processing. It explores how the effective garage of information can essentially aid in depth information entry and queries, which permits refined research. It additionally appears at how information processing and visualization support to speak info sincerely and successfully. the second one a part of the e-book is dedicated to the extraction of crucial info and the prediction of websites. The publication indicates how colossal info research can be utilized to appreciate the pursuits, place, and seek background of clients and supply extra exact predictions of consumer habit. The latter components of the e-book disguise the security of privateness and safety, and emergent purposes of huge info and social networks. It analyzes the best way to version rumor diffusion, determine incorrect information from monstrous information, and layout intervention options. functions of massive info and social networks in multilayer networks and multiparty platforms also are lined in-depth.

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Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 123-131, 2002. 46. J. Walter, J. Ontrup, D. Wessling, H. Ritter, “Interactive Visualization and Navigation in Large Data Collections Using the Hyperbolic Space”, in Proc. of the 3rd IEEE International Conference on Data Mining (ICDM), pp. 355-362, Nov. 2003. 47. J. Lamping, R. Rao, “Laying out and Visualizing Large Trees Using a Hyperbolic Space”, in Proc. ACM Symp User Interface Software and Technology, pp. 13-14, 1994. 48. J. Bingham, S.

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695-698, Feb. 2009. 13. D. A. Tran, K. Vut, “Dimensionality Reduction in Hyperbolic Data Spaces: Bounding Reconstructed-Information Loss”, in Proc. of 7th IEEE/ACIS Int’l Conf. on Computer and Information Science, pp. 133-139, May 2008. 14. R. Lior, O. Maimon, “Clustering methods”, Data Mining and Knowledge Discovery Handbook, Springer US, pp. 321-352, 2005. 15. D. Yan, L. Huang, M. I. Jordan, “Fast Approximate Spectral Clustering”, in Proc. of the 15th ACM Conference on Knowledge Discovery and Data Mining (SIGKDD), Paris, France, 2009.

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