By My T. Thai, Weili Wu, Hui Xiong
This ebook provides contemporary advancements at the theoretical, algorithmic, and alertness points of massive information in advanced and Social Networks. The booklet comprises 4 elements, overlaying a variety of themes. the 1st a part of the booklet makes a speciality of info garage and knowledge processing. It explores how the effective garage of information can essentially help extensive facts entry and queries, which permits subtle research. It additionally appears to be like at how info processing and visualization support to speak info basically and successfully. the second one a part of the booklet is dedicated to the extraction of crucial details and the prediction of web pages. The ebook exhibits how great facts research can be utilized to appreciate the pursuits, place, and seek heritage of clients and supply extra actual predictions of person habit. The latter elements of the e-book conceal the security of privateness and defense, and emergent purposes of massive information and social networks. It analyzes how one can version rumor diffusion, establish incorrect information from great facts, and layout intervention recommendations. purposes of huge facts and social networks in multilayer networks and multiparty structures also are coated in-depth.
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Extra info for Big data in complex and social networks
The hyperbolic space grows exponentially with its radius around each point. 5). ii. , via the mouse. There are many visualization techniques that utilize hyperbolic space embedding. Most of them focus on hierarchical or tree-like graph embedding. Generally, depending on the data representation, different techniques can be applied, as described in the following. 1 Adaptive Focus in Hyperbolic Space The visualization of large datasets in general suffers from a difficulty to show both focus and global context.
Crovella, “Hyperbolic Embedding and Routing for Dynamic Graphs”, IEEE INFOCOM, pp. 1647-1655, April 2009. 12. I. Benjamini, Y. Makarychev, “Dimension Reduction for Hyperbolic Space”, American Mathematical Society, Vol. 137, No. 2, pp. 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.
In our example, the first line defines an outer bag input and loads a text document from HDFS. Each line of this text file is declared as string (chararray in Pig Latin). 3 WordCount written in Pig . 42 Big Data in Complex and Social Networks built-in function TOKENIZE and relational statement FILTER. The fourth line aggregates instances of the exact same word together and constructs a two-cell tuple for each word. Here, the first cell of this tuple stores the text of this word, while the second cell stores a list of the same word.
Big data in complex and social networks by My T. Thai, Weili Wu, Hui Xiong