Download e-book for kindle: Graph-based social media analysis by Ioannis Pitas

By Ioannis Pitas

Focused at the mathematical foundations of social media research, Graph-Based Social Media Analysis presents a entire creation to using graph research within the examine of social and electronic media. It addresses a huge medical and technological problem, specifically the confluence of graph research and community thought with linear algebra, electronic media, computer studying, mammoth information research, and sign processing.

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

The publication emphasizes the massive info points of social and electronic media. It provides quite a few techniques to storing big quantities of information on-line and retrieving that facts in real-time. It demystifies complicated social media phenomena, comparable to details diffusion, advertising and advice platforms in social media, and evolving platforms. It additionally covers rising traits, akin to sizeable facts research and social media evolution.

Describing easy methods to behavior right research of the social and electronic media markets, the booklet presents insights into processing, storing, and visualizing massive social media facts 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 accordance with score, label propagation and diffusion in social media, graph-based development acceptance and laptop studying, graph-based development class and dimensionality aid, and masses more.

This booklet is a perfect reference for scientists and engineers operating in social media and electronic media construction 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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Random walks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Graph anomaly detection . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Spectral anomaly detection . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bibliography . . . . . . . . . . .

35 36 36 37 38 40 41 41 43 45 46 48 48 50 51 53 54 54 56 57 58 59 Introduction Graphs provide an intuitive way of representing connected or interacting entities [GY99]. , web pages linking to each other [BV03], friendship in social networks, the relative location of image features [DPZ01], transportation networks, electric circuits and computer network topologies. Mathematicians have been studying graph theory since the 18th century, starting with a published paper on the Seven Bridges of Konigsberg by Euler [Big93].

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

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