Graph-based social media analysis by Ioannis Pitas

Machine Theory

By Ioannis Pitas

Focused at the mathematical foundations of social media research, Graph-Based Social Media Analysis presents a accomplished advent to using graph research within the research of social and electronic media. It addresses an immense clinical and technological problem, specifically the confluence of graph research and community thought with linear algebra, electronic media, desktop studying, tremendous info research, and sign processing.

Supplying an summary of graph-based social media research, the booklet presents readers with a transparent knowing of social media constitution. It makes use of graph idea, quite the algebraic description and research of graphs, in social media studies.

The publication emphasizes the massive info facets of social and electronic media. It offers a number of techniques to storing monstrous quantities of knowledge on-line and retrieving that information in real-time. It demystifies complicated social media phenomena, akin to details diffusion, advertising and suggestion platforms in social media, and evolving platforms. It additionally covers rising traits, corresponding to huge facts research and social media evolution.

Describing the best way to behavior right research of the social and electronic media markets, the publication presents insights into processing, storing, and visualizing large social media information and social graphs. It contains assurance of graphs in social and electronic media, graph and hyper-graph basics, mathematical foundations coming from linear algebra, algebraic graph research, graph clustering, group detection, graph matching, net seek in accordance with rating, label propagation and diffusion in social media, graph-based trend popularity and desktop studying, graph-based trend category and dimensionality relief, and lots more and plenty more.

This ebook is a perfect reference for scientists and engineers operating in social media and electronic media creation and distribution. it's also compatible to be used as a textbook in undergraduate or graduate classes on electronic media, social media, or social networks.

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Graph-based social media analysis

Interested by the mathematical foundations of social media research, Graph-Based Social Media research presents a complete creation to using graph research within the research of social and electronic media. It addresses a tremendous clinical and technological problem, specifically the confluence of graph research and community concept with linear algebra, electronic media, computer studying, vast info research, and sign processing.

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1 Spectral graph matching . . . . . . . . . . . . . . . . . . . . . . . 2 Frequent subgraph mining . . . . . . . . . . . . . . . . . . . . . . . Random walks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Graph anomaly detection . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Spectral anomaly detection . . . .

Namely, it is equal to the scaled column of matrix V, x = λvi , whose corresponding singular value is zero di = 0. Since the rank of matrix A is rank(A) = M − 1, all other singular values are non zero (positive). In practice, due to numerical errors, we choose the singular value di that has the lowest absolute value (close to zero). 1: Cholesky decomposition of matrix A. provided that matrix A is non-singular. Numerous methods exist for the numerical solution of linear equation systems [Str88, Mey00], whose presentation is beyond the overview provided by this section.

3 Spectral graph generation . . . . . . . . . . . . . . . . . . . . . . . Graph clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Global clustering algorithms . . . . . . . . . . . . . . . . . . . . . 2 Local clustering algorithms . . . . . . . . . . . . . . . . . . . . . . 3 Spectral clustering algorithms . . . . . . .

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