Download Graph-based social media analysis by Ioannis Pitas PDF

By Ioannis Pitas

Focused at the mathematical foundations of social media research, Graph-Based Social Media Analysis presents a finished creation 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, monstrous info research, and sign processing.

Supplying an outline of graph-based social media research, the publication presents readers with a transparent realizing of social media constitution. It makes use of graph conception, fairly the algebraic description and research of graphs, in social media studies.

The booklet emphasizes the large information points of social and electronic media. It provides quite a few methods to storing tremendous quantities of information on-line and retrieving that facts in real-time. It demystifies advanced social media phenomena, akin to details diffusion, advertising and suggestion platforms in social media, and evolving structures. It additionally covers rising traits, akin to sizeable information research and social media evolution.

Describing how one can behavior right research of the social and electronic media markets, the e-book presents insights into processing, storing, and visualizing immense social media information and social graphs. It comprises insurance of graphs in social and electronic media, graph and hyper-graph basics, mathematical foundations coming from linear algebra, algebraic graph research, graph clustering, neighborhood detection, graph matching, net seek in keeping with rating, label propagation and diffusion in social media, graph-based development acceptance and computer studying, graph-based development type and dimensionality relief, and masses more.

This booklet 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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Matrix decompositions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vector and matrix derivatives . . . . . . . . . . . . . . . . . . . . . . . . . Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Graph basics 21 24 28 31 31 Formally, a graph G = (V, E) is an ordered pair of a set of vertices V = {vi } and a set of edges E ⊆ V × V [BM76, Wes01].

The normalized Laplacian matrix of a hypergraph is defined accordingly: 1 1 L = I − D−1/2 HWHT D−1/2 = I − D−1/2 AD−1/2 . 8) Linear algebra tools Linear algebra [Str88] plays an important role in graph analysis. Therefore, its tools are reviewed in this section. There are various products defined between two matrices. The matrix product Z XY of N × P matrix X and P × M matrix Y is the N × M matrix Z, whose elements are given by: P zij = xik ykj . 1) k=1 The Hadamard product Z X ◦ Y of N × M matrices X and Y is the N × M matrix Z, whose elements are given by: zij = xij yij .

1 Global clustering algorithms . . . . . . . . . . . . . . . . . . . . . 2 Local clustering algorithms . . . . . . . . . . . . . . . . . . . . . . 3 Spectral clustering algorithms . . . . . . . . . . . . . . . . . . . . . 4 Overlapping community detection . . . . . . . . . . . . . . . . . . Graph matching . . . . . . . . . . . . . . . . . . . . . . . .

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