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Posrednost vozlišč v omrežjih in uporaba v scientometriki
ID Kavčič, Luka (Author), ID Šubelj, Lovro (Mentor) More about this mentor... This link opens in a new window

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Abstract
V zadnjem stoletju se je število raziskovalcev in izdanih znanstvenih del močno povečalo. Sledenje znanstvenim odkritjem ter določanje njihovega vpliva postaja vse težje. Poleg tega raziskovalci vse bolj tekmujejo pri pridobivanju ugleda v znanosti. Zato je nastala potreba po avtomatski in zanesljivi meri za rangiranje znanstvenih del. Omrežja citiranj omogočajo dober vpogled v potek razvoja znanosti. S pomočjo mer, kot je posrednost vozlišč, lahko določimo, katera dela so igrala pomembnejšo vlogo pri razvoju poljubnega znanstvenega področja. V magistrski nalogi predstavimo mero posrednosti vozlišč in jo razširimo na utežene grafe ter preverimo več načinov slikanja uteži na povezavah v verjetnosti. V delu predstavimo tudi Monte Carlo algoritem za izračun mere in implementiramo spletno aplikacijo. Na koncu preverimo, kako različne lastnosti člankov vplivajo na samo delovanje razširjene mere, ter jih empirično ovrednotimo. V primerjavi z osnovno mero naša daje nekoliko boljše rezultate.

Language:Slovenian
Keywords:omrežja citiranj, posrednost vozlišč, razširjena mera, aplikacije
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FRI - Faculty of Computer and Information Science
Year:2021
PID:20.500.12556/RUL-133582 This link opens in a new window
COBISS.SI-ID:88505091 This link opens in a new window
Publication date in RUL:02.12.2021
Views:867
Downloads:101
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Secondary language

Language:English
Title:Intermediacy of nodes in networks and applications in scientometrics
Abstract:
In the last century, the number of researchers and published scientific works has greatly increased. Tracking scientific discoveries and determining their impact is becoming increasingly difficult. In addition, researchers are increasingly competing in gaining a reputation in science. Therefore, there was a need for an automatic and reliable measure for ranking scientific works. Citation networks provide a good insight into the development of science. With the help of measures such as the node intermediacy, we can determine which scientific works played a more important role in the development of a given scientific field. In the master's thesis we present the node intermediacy measure and extend it to weighted graphs. We then evaluate several ways of mapping the weights of connections to probabilities. We also present the Monte Carlo algorithm for calculating the measure and implement a web application. Finally, we examine how the different properties of the articles affect the extended measure of node intermediacy and evaluate them empirically. Compared to the original measure, our gives slightly better results.

Keywords:citation networks, node intermediacy, extended measure, applications

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