Analiza skupnosti na družbenem omrežju igralcev in filmov

diplomska naloga

Simon Retelj (Author), Zoran Levnajić (Mentor)

Abstract

Z razvojem spleta je znanost analize omrežja dosegla zlato dobo, saj imamo na razpolago neomejeno število enostavno dostopnih podatkov. S popularnostjo in razvojem družbenih omrežij pa je prišla v ospredje tudi analiza družbenih omrežij. Cilj te diplomske naloge je ustvariti omrežje filmov in omrežje igralcev, katerih podatke bomo pridobili iz podatkovne baze spletne strani IMDb. To nam bo omogočila knjižnica IMDb.Py programskega jezika Python, v katerem bo potekala tudi nadaljnja analiza obeh omrežij. Nad omrežjema bomo pognali štiri najbolj popularne mere centralnosti, in sicer Degree, Closeness, Betweenness in Eigenvector. Te bomo med seboj primerjali in poskušali odkriti, ali med njimi obstaja kakšna povezava. Prav tako bomo preverili, ali obstaja kakšna povezava med omrežjema glede centralnosti. V omrežjih bomo poskušali odkriti skupnosti; to bomo storili z algoritmoma Louvain in CNM. Iz pridobljenih skupnosti bomo skušali ugotoviti razlike med njimi na podlagi podatkov, ki so nam na voljo.

Keywords

podatki;analiza družbenih omrežij;centranost;skupnosti;Python;Louvain;CNM;IMDb;

Data

Language:	Slovenian
Year of publishing:	2016
Typology:	2.11 - Undergraduate Thesis
Organization:	FIŠ - Faculty of Information Studies
Publisher:	[S. Retelj]
UDC:	004.7:316.472.4(043.2)
COBISS:	2048407827
Views:	164
Downloads:	23
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	English
Secondary abstract:	Network analysis has entered gold age with the developemnt of the internet, mostly beacuse of all the easy accesable data. The popularity of online networks has brought to the foree the analysis of social networks. The objective of our thesis is to build network of movies and network of actors, for which we will collect data from IMDb database. We will access the base through package IMDb.Py which is made for Python programming language. All further analysis of the networks will be made in Python. First of all we will run Degree, Closeness, Betweenness and Eigenvecto centrality over both networks. We will compare centralitys and try to find out if there exist any corelation between them. We will also compare centralitys between networks and try to find the connection there. As last we will try to find communities within networks, by running Louvain and CNM algorithms over them and find differences between communites with the data that is available to us.
Secondary keywords:	data;social network analysis;centrality;community;Python;Louvain;CNM;IMDb;
Type (COBISS):	Bachelor thesis/paper
Thesis comment:	Fakulteta za informacijske študije v Novem mestu
Source comment:	Na ov.: Diplomska naloga : visokošolskega strokovnega študijskega programa prve stopnje;
Pages:	63 str.
ID:	10954632