PROPERTIES OF AUTOSEMANTIC WORD NETWORKS IN UKRAINIAN TEXTS

2019 ◽  
Vol 22 (06) ◽  
pp. 1950016
Author(s):  
SOLOMIJA BUK ◽  
YURI KRYNYTSKYI ◽  
ANDRIJ ROVENCHAK
Keyword(s):  
Network Analysis ◽  
Degree Distribution ◽  
Small World ◽  
Parts Of Speech ◽  
Scale Free ◽  
Network Parameters ◽  
Network Diameter

We present results of network analysis of Ukrainian texts. Autosemantic (meaningful) words are considered as network vertices connected with links when belonging to one sentence. Subnetworks corresponding to specific parts of speech (verbs, nouns, adjectives, etc.) are also built. The obtained networks are small-world and scale-free. To make comparisons, random texts with parameters corresponding to real texts are generated using several approaches. Various parameters of networks are calculated, including transitivity, betweenness, degree centralization, mean distance, network diameter, exponents of degree distribution, etc. Comparison of network parameters of real and generated texts shows that borders between them are quite fuzzy.

Top Museums on Instagram

2019 ◽  
Vol 3 (2) ◽  
pp. 18-42 ◽  
Author(s):  
Vasiliki G. Vrana ◽  
Dimitrios A. Kydros ◽  
Evangelos C. Kehris ◽  
Anastasios-Ioannis T. Theocharidis ◽  
George I. Kavavasilis
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Small World ◽  
Marketing Strategies ◽  
The Other ◽  
Small World Network ◽  
Scale Free ◽  
Centrality Metrics ◽  
Photo Sharing ◽  
Visualization Algorithms

Pictures speak louder than words. In this fast-moving world where people hardly have time to read anything, photo-sharing sites become more and more popular. Instagram is being used by millions of people and has created a “sharing ecosystem” that also encourages curation, expression, and produces feedback. Museums are moving quickly to integrate Instagram into their marketing strategies, provide information, engage with audience and connect to other museums Instagram accounts. Taking into consideration that people may not see museum accounts in the same way that the other museum accounts do, the article first describes accounts' performance of the top, most visited museums worldwide and next investigates their interconnection. The analysis uses techniques from social network analysis, including visualization algorithms and calculations of well-established metrics. The research reveals the most important modes of the network by calculating the appropriate centrality metrics and shows that the network formed by the museum Instagram accounts is a scale–free small world network.

SCALE-FREE AND SMALL-WORLD PROPERTIES OF A SPECIAL HIERARCHICAL NETWORK

Fractals ◽  
2019 ◽  
Vol 27 (02) ◽  
pp. 1950010
Author(s):  
DAOHUA WANG ◽  
YUMEI XUE ◽  
QIAN ZHANG ◽  
MIN NIU
Keyword(s):  
Degree Distribution ◽  
Path Length ◽  
Small World ◽  
Average Path Length ◽  
Clustering Coefficient ◽  
Hierarchical Network ◽  
Scale Free

Many real systems behave similarly with scale-free and small-world structures. In this paper, we generate a special hierarchical network and based on the particular construction of the graph, we aim to present a study on some properties, such as the clustering coefficient, average path length and degree distribution of it, which shows the scale-free and small-world effects of this network.

The Research of Complex Network on Archives Blog

2011 ◽  
Vol 181-182 ◽  
pp. 14-18
Author(s):  
Yi He
Keyword(s):  
Information Sharing ◽  
Complex Network ◽  
Network Model ◽  
Degree Distribution ◽  
Small World ◽  
Scale Free ◽  
Set Up

At the background of archives blog on Internet, this paper constructs a directed complex network model, and analyzes the network characters such as degree distribution. To verify its efficiency, we collect blogs’ information and set up a complex network..From the analysis result of the simulation and demonstration network, we know that they have the same characters, which show that, the virtual society network has small-world effect and scale-free character compared with real society network. The results indicate that the establishment of archives blog is favor to spread rapidly archives information, improve information sharing efficiency and promote the development of archives technology.

scholarly journals Syntactic Networks of Interlanguage Across L2 Modalities and Proficiency Levels

2021 ◽  
Vol 12 ◽  
Author(s):  
Yuxin Hao ◽  
Xuelin Wang ◽  
Meng Wu ◽  
Haitao Liu
Keyword(s):  
Native Speakers ◽  
First Language ◽  
Small World ◽  
Scale Free ◽  
Ideal Object ◽  
Proficiency Levels ◽  
Network Parameters ◽  
Quantitative Studies ◽  
L2 Development ◽  
Learner Language

Over time, interlanguage studies have shifted from early qualitative to quantitative studies of specific linguistic structures. However, the focus of these studies is usually on one aspect of an interlanguage instead of the whole system. The ideal object of interlanguage research is a second language (L2) learner language system, for only in this way can the entire L2 learning process can be examined. As a self-organizing and self-regulated system, the panorama of interlanguage can be revealed objectively through a complex network approach. In this study, we construct eight interlanguage dependency syntactic networks of varying proficiency levels and modalities, and conduct a quantitative study of respective network parameters. We find that all syntactic networks of Chinese L2 learners (English native speakers) initially present scale-free and small-world properties. Additionally, there is no sudden syntactic emergence in interlanguage with different modalities. This suggests varying regularities in the development of a syntactic network between interlanguage and native language acquisition. Moreover, the first language plays an important role in L2 development. The network parameters (<k>), L, C, ND, and NC can differentiate interlanguage modalities, and five quantitative parameters, <k>, C, ND, γ′, and NC, can indicate L2 proficiency.

scholarly journals A Study on Differences between Simplified and Traditional Chinese Based on Complex Network Analysis of the Word Co-Occurrence Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Zhongqiang Jiang ◽  
Dongmei Zhao ◽  
Jiangbin Zheng ◽  
Yidong Chen
Keyword(s):  
Network Analysis ◽  
Complex Network ◽  
Small World ◽  
Motif Analysis ◽  
Scale Free ◽  
Complex Network Analysis ◽  
Simplified Chinese ◽  
Language Analysis ◽  
Corpus Size ◽  
Global Understanding

Currently, most work on comparing differences between simplified and traditional Chinese only focuses on the character or lexical level, without taking the global differences into consideration. In order to solve this problem, this paper proposes to use complex network analysis of word co-occurrence networks, which have been successfully applied to the language analysis research and can tackle global characters and explore the differences between simplified and traditional Chinese. Specially, we first constructed a word co-occurrence network for simplified and traditional Chinese using selected news corpora. Then, the complex network analysis methods were performed, including network statistics analysis, kernel lexicon comparison, and motif analysis, to gain a global understanding of these networks. After that, the networks were compared based on the properties obtained. Through comparison, we can obtain three interesting results: first, the co-occurrence networks of simplified Chinese and traditional Chinese are both small-world and scale-free networks. However, given the same corpus size, the co-occurrence networks of traditional Chinese tend to have more nodes, which may be due to a large number of one-to-many character/word mappings from simplified Chinese to traditional Chinese; second, since traditional Chinese retains more ancient Chinese words and uses fewer weak verbs, the traditional Chinese kernel lexicons have more entries than the simplified Chinese kernel lexicons; third, motif analysis shows that there is no difference between the simplified Chinese network and the corresponding traditional Chinese network, which means that simplified and traditional Chinese are semantically consistent.

Top Museums on Instagram

2021 ◽  
pp. 1029-1055
Author(s):  
Vasiliki G. Vrana ◽  
Dimitrios A. Kydros ◽  
Evangelos C. Kehris ◽  
Anastasios-Ioannis T. Theocharidis ◽  
George I. Kavavasilis
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Small World ◽  
Marketing Strategies ◽  
The Other ◽  
Small World Network ◽  
Scale Free ◽  
Centrality Metrics ◽  
Photo Sharing ◽  
Visualization Algorithms

Pictures speak louder than words. In this fast-moving world where people hardly have time to read anything, photo-sharing sites become more and more popular. Instagram is being used by millions of people and has created a “sharing ecosystem” that also encourages curation, expression, and produces feedback. Museums are moving quickly to integrate Instagram into their marketing strategies, provide information, engage with audience and connect to other museums Instagram accounts. Taking into consideration that people may not see museum accounts in the same way that the other museum accounts do, the article first describes accounts' performance of the top, most visited museums worldwide and next investigates their interconnection. The analysis uses techniques from social network analysis, including visualization algorithms and calculations of well-established metrics. The research reveals the most important modes of the network by calculating the appropriate centrality metrics and shows that the network formed by the museum Instagram accounts is a scale–free small world network.

Embedding funding consultation in library services

2018 ◽  
Vol 36 (3) ◽  
pp. 378-399 ◽  
Author(s):  
Jiang Wu ◽  
Jingxuan Cai ◽  
Miao Jin ◽  
Ke Dong
Keyword(s):  
Network Analysis ◽  
Small World ◽  
Knowledge Flow ◽  
Lower Probability ◽  
Library Services ◽  
Flow Network ◽  
Content Type ◽  
Scale Free ◽  
Interdisciplinary Knowledge ◽  
Funding Application

Purpose Although interdisciplinary research is an increasing trend in scientific funding projects, they are suffering from a lower probability of being funded. The purpose of this paper is to analyze the current situation on successful case of funding application and provides suggestions on how libraries can expand services to help scientific funding application. Design/methodology/approach This paper utilizes the co-occurrences of disciplinary application codes to construct an interdisciplinary knowledge flow network. Based on 193517 sponsored projects of the National Natural Science Foundation of China, the authors study the interdisciplinary flow of knowledge and investigate the evolution of network structure using social network analysis. Findings Results show that the interdisciplinary knowledge flow network is not only a small-world network but also a scale-free network. Two main knowledge flow paths across scientific departments exist, showing the heterogeneity of knowledge distributions across scientific disciplines. The authors also find that if two disciplines in the same scientific department both have a wide influence to other disciplines, they are more prone to link together and create a knowledge chain. Originality/value Funding consultation currently has not occupied an advisory role either in library services or in the research team. This paper conducts a co-occurrences network analysis of interdisciplinary knowledge flow in scientific funding projects. Considering the complexity of funding application and the advantage of traditional library services on information collection, integration, and utilization, the authors conclude the possibility and necessity of embedding funding consultation in traditional library services.

scholarly journals Polygon-Based Hierarchical Planar Networks Based on Generalized Apollonian Construction

Physics ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 998-1014
Author(s):  
Mikhail Tamm ◽  
Dmitry Koval ◽  
Vladimir Stadnichuk
Keyword(s):  
Complex Networks ◽  
Power Law ◽  
Degree Distribution ◽  
Small World ◽  
Scale Free ◽  
Model Network ◽  
Suggested Procedure ◽  
Similar Construction ◽  
Scale Free Graphs ◽  
Planar Networks

Experimentally observed complex networks are often scale-free, small-world and have an unexpectedly large number of small cycles. An Apollonian network is one notable example of a model network simultaneously having all three of these properties. This network is constructed by a deterministic procedure of consequentially splitting a triangle into smaller and smaller triangles. In this paper, a similar construction based on the consequential splitting of tetragons and other polygons with an even number of edges is presented. The suggested procedure is stochastic and results in the ensemble of planar scale-free graphs. In the limit of a large number of splittings, the degree distribution of the graph converges to a true power law with an exponent, which is smaller than three in the case of tetragons and larger than three for polygons with a larger number of edges. It is shown that it is possible to stochastically mix tetragon-based and hexagon-based constructions to obtain an ensemble of graphs with a tunable exponent of degree distribution. Other possible planar generalizations of the Apollonian procedure are also briefly discussed.

Utilising social network analysis to study the characteristics and functions of the co-occurrence network of online tags

2014 ◽  
Vol 38 (2) ◽  
pp. 232-247 ◽  
Author(s):  
Ma Feicheng ◽  
Li Yating
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Small World ◽  
Online Resources ◽  
Social Tagging ◽  
The Internet ◽  
Content Type ◽  
Categorical Analysis ◽  
Scale Free

Purpose – This paper aims to explore the characteristics of the co-occurrence network of online tags and propose new approaches of applying social network analysis by utilising social tagging in order to organise data. Design/methodology/approach – The authors collected online resources labelled “tag” from 7 November 2004 to 31 October 2011 from the CiteULike website, comprising 684 papers and their URLs, titles and data on tagging (users, times, and tags). They examined the co-occurrence network of online tags by using the analyses of social networks, including the analysis of coherence, the analysis of centricity and core to periphery categorical analysis. Findings – Some features of the co-occurrence of online tags are as follows: the internet is subject to the “small world” phenomenon, as well as being “scale-free”. The structure of the internet reflects stable areas of core knowledge. In addition to five possible applications of social network analysis, social tagging has the greatest significance in organising online resources. Originality/value – This research finds that co-occurrence of tags online is an effective way to organise and index data. Some suggestions are provided on the organisation of online resources.

scholarly journals Exploring Interdisciplinary Relationships Among King Abdulaziz University Departments via ResearchGate: Network Analysis and Visualizations

2020 ◽  
Vol 11 (3) ◽  
pp. 55
Author(s):  
Hanan M. Baaqeel ◽  
Sara F. Aloufi ◽  
Tariq Elyas
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Random Graph ◽  
Degree Distribution ◽  
Small World ◽  
Education Sector ◽  
Graph Models ◽  
Random Graph Models ◽  
King Abdulaziz University ◽  
Academic Social Network

Because all disciplines are connected, interdisciplinary studies are one of the most significant discussions in the education sector. It involves the merging of two or more academic disciplines into one activity. The aim of this research paper is to explore the relationship of interdisciplinary research and network among all departments at King Abdulaziz University (KAU) in ResearchGate (RG) by using the statistical network analysis of undirected social networks. In our academic network, the departments of the university represent the vertices and their academic relationships. We will detect the communities between the departments in RG network by using statistical analysis of the network for each community. Finally, we will compare the academic social network at KAU to some random graph models, and investigate some random graph characteristics, such as power-law, small-world, and scale-free models. In our research, we found that the Department of Chemistry has the highest degree for the academic social network at KAU in RG, and the highest eigenvector centrality as well. In terms of vertex centrality, the Department of Electrical and Computer Engineering has the highest value in closeness and betweenness centrality. Also, we found that the most two connected departments are the Department of Computer Science and Department of Physics through the edge weight equals 248. By using community detection, we found there are seven communities. We conclude that the degree distribution of the academic social network of KAU in RG is different from the degree distribution of random graph models, but it is slightly close to small world model. This study , in turn, can participate to achieve one of the goals of Vision 2030 by shedding some light into how to improve research networks in the education sector and research among Saudi universities.

Sign in / Sign up

Export Citation Format

Share Document