Social Networks and Questions of Big Data. Graph search for communities with corresponding keywords


Exposé Écrit pour un Séminaire / Cours, 2017

5 Pages, Note: 1.3


Résumé ou Introduction

This essay deals with a graph search for communities with corresponding keywords.

The era of big data and world-spanning social networks has highlighted the necessity of ways to make sense of this vast amount of information. Data can be arranged in a graph of connected vertices, therefore giving it a basic structure. If the vertices are further described by keywords, the structure is called an attributed graph. This paper discusses a query algorithm that scans these attributed graphs for communities that are not only structurally linked - therefore forming subgraphs - but also share the same keywords. This method might give new insights into the composition of large networks, highlight interesting connections and give opportunities for effectively targeted marketing. As a specific use case, the idea of the attributed community query is applied to the example of a film recommendation program.

Résumé des informations

Titre
Social Networks and Questions of Big Data. Graph search for communities with corresponding keywords
Université
LMU Munich  (Institut für Informatik)
Cours
Recent Developments in Data Science
Note
1.3
Auteur
Année
2017
Pages
5
N° de catalogue
V369515
ISBN (ebook)
9783668471757
Taille d'un fichier
572 KB
Langue
anglais
Mots clés
community, graph, network, search, mining, social network, data, informatics
Citation du texte
Andrea Attwenger (Auteur), 2017, Social Networks and Questions of Big Data. Graph search for communities with corresponding keywords, Munich, GRIN Verlag, https://www.grin.com/document/369515

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