94 research outputs found
Comparison of the language networks from literature and blogs
In this paper we present the comparison of the linguistic networks from
literature and blog texts. The linguistic networks are constructed from texts
as directed and weighted co-occurrence networks of words. Words are nodes and
links are established between two nodes if they are directly co-occurring
within the sentence. The comparison of the networks structure is performed at
global level (network) in terms of: average node degree, average shortest path
length, diameter, clustering coefficient, density and number of components.
Furthermore, we perform analysis on the local level (node) by comparing the
rank plots of in and out degree, strength and selectivity. The
selectivity-based results point out that there are differences between the
structure of the networks constructed from literature and blogs
Multilayer Network of Language: a Unified Framework for Structural Analysis of Linguistic Subsystems
Recently, the focus of complex networks research has shifted from the
analysis of isolated properties of a system toward a more realistic modeling of
multiple phenomena - multilayer networks. Motivated by the prosperity of
multilayer approach in social, transport or trade systems, we propose the
introduction of multilayer networks for language. The multilayer network of
language is a unified framework for modeling linguistic subsystems and their
structural properties enabling the exploration of their mutual interactions.
Various aspects of natural language systems can be represented as complex
networks, whose vertices depict linguistic units, while links model their
relations. The multilayer network of language is defined by three aspects: the
network construction principle, the linguistic subsystem and the language of
interest. More precisely, we construct a word-level (syntax, co-occurrence and
its shuffled counterpart) and a subword level (syllables and graphemes) network
layers, from five variations of original text (in the modeled language). The
obtained results suggest that there are substantial differences between the
networks structures of different language subsystems, which are hidden during
the exploration of an isolated layer. The word-level layers share structural
properties regardless of the language (e.g. Croatian or English), while the
syllabic subword level expresses more language dependent structural properties.
The preserved weighted overlap quantifies the similarity of word-level layers
in weighted and directed networks. Moreover, the analysis of motifs reveals a
close topological structure of the syntactic and syllabic layers for both
languages. The findings corroborate that the multilayer network framework is a
powerful, consistent and systematic approach to model several linguistic
subsystems simultaneously and hence to provide a more unified view on language
Complex Networks Measures for Differentiation between Normal and Shuffled Croatian Texts
This paper studies the properties of the Croatian texts via complex networks.
We present network properties of normal and shuffled Croatian texts for
different shuffling principles: on the sentence level and on the text level. In
both experiments we preserved the vocabulary size, word and sentence frequency
distributions. Additionally, in the first shuffling approach we preserved the
sentence structure of the text and the number of words per sentence. Obtained
results showed that degree rank distributions exhibit no substantial deviation
in shuffled networks, and strength rank distributions are preserved due to the
same word frequencies. Therefore, standard approach to study the structure of
linguistic co-occurrence networks showed no clear difference among the
topologies of normal and shuffled texts. Finally, we showed that the in- and
out- selectivity values from shuffled texts are constantly below selectivity
values calculated from normal texts. Our results corroborate that the node
selectivity measure can capture structural differences between original and
shuffled Croatian texts
TAKSONOMIJA METODA AKADEMSKOG PLAGIRANJA
The article gives an overview of the plagiarism domain, with focus on academic plagiarism. The
article defines plagiarism, explains the origin of the term, as well as plagiarism related terms. It
identifies the extent of the plagiarism domain and then focuses on the plagiarism subdomain of text
documents, for which it gives an overview of current classifications and taxonomies and then proposes a
more comprehensive classification according to several criteria: their origin and purpose, technical
implementation, consequence, complexity of detection and according to the number of linguistic sources.
The article suggests the new classification of academic plagiarism, describes sorts and methods of
plagiarism, types and categories, approaches and phases of plagiarism detection, the classification
of methods and algorithms for plagiarism detection. The title of the article explicitly targets the
academic community, but it is sufficiently general and interdisciplinary, so it can be useful for
many other professionals like software developers, linguists and librarians.Rad daje pregled domene plagiranja tekstnih dokumenata. Opisuje porijeklo pojma plagijata, daje prikaz
definicija te objašnjava plagijatu srodne pojmove. Ukazuje na širinu domene plagiranja, a za tekstne
dokumenate daje pregled dosadašnjih taksonomija i predlaže sveobuhvatniju taksonomiju prema više kriterija:
porijeklu i namjeni, tehničkoj provedbi plagiranja, posljedicama plagiranja, složenosti otkrivanja i
(više)jezičnom porijeklu. Rad predlaže novu klasifikaciju akademskog plagiranja, prikazuje vrste i
metode plagiranja, tipove i kategorije plagijata, pristupe i faze otkrivanja plagiranja. Potom opisuje
klasifikaciju metoda i algoritama otkrivanja plagijata. Iako cilja na akademskog čitatelja, može biti
od koristi u interdisciplinarnim područjima te razvijateljima softvera, lingvistima i knjižničarima
The Complex Network Patterns of Human Migration at Different Geographical Scales: Network Science meets Regression Analysis
Migration's influence in shaping population dynamics in times of impending
climate and population crises exposes its crucial role in upholding societal
cohesion. As migration impacts virtually all aspects of life, it continues to
require attention across scientific disciplines. This study delves into two
distinctive substrates of Migration Studies: the "why" substrate, which deals
with identifying the factors driving migration relying primarily on regression
modeling, encompassing economic, demographic, geographic, cultural, political,
and other variables; and the "how" substrate, which focuses on identifying
migration flows and patterns, drawing from Network Science tools and
visualization techniques to depict complex migration networks. Despite the
growing percentage of Network Science studies in migration, the explanations of
the identified network traits remain very scarce, highlighting the detachment
between the two research substrates. Our study includes real-world network
analyses of human migration across different geographical levels: city,
country, and global. We examine inter-district migration in Vienna at the city
level, review internal migration networks in Austria and Croatia at the country
level, and analyze migration exchange between Croatia and the world at the
global level. By comparing network structures, we demonstrate how distinct
network traits impact regression modeling. This work not only uncovers
migration network patterns in previously unexplored areas but also presents a
comprehensive overview of recent research, highlighting gaps in each field and
their interconnectedness. Our contribution offers suggestions for integrating
both fields to enhance methodological rigor and support future research.Comment: 25 pages,6 figure
- …