scholarly journals DIFFERENCES BETWEEN NORMAL AND SHUFFLED TEXTS: STRUCTURAL PROPERTIES OF WEIGHTED NETWORKS

2009 ◽  
Vol 12 (01) ◽  
pp. 113-129 ◽  
Author(s):  
A. P. MASUCCI ◽  
G. J. RODGERS
Keyword(s):  
Social Network ◽  
Structural Properties ◽  
Scientific Collaboration ◽  
Ad Hoc ◽  
Strength Distribution ◽  
Collaboration Network ◽  
Scale Free ◽  
Weighted Networks ◽  
Free Weight ◽  
Growing Network

In this paper we deal with the structural properties of weighted networks. Starting from an empirical analysis of a linguistic network, we analyze the differences between the statistical properties of a real and a shuffled network. We show that the scale-free degree distribution and the scale-free weight distribution are induced by the scale-free strength distribution, that is Zipf's law. We test the result on a scientific collaboration network, that is a social network, and we define a measure – the vertex selectivity – that can distinguish a real network from a shuffled network. We prove, via an ad hoc stochastic growing network with second order correlations, that this measure can effectively capture the correlations within the topology of the network.

scholarly journals Scientific collaboration network ammong Brazilian universities

2013 ◽  
Vol 6 (1) ◽  
Author(s):  
Maria Isabel Escalona-Fernandez ◽  
Antonio Pulgarin-Guerrero ◽  
Ely Francina Tannuri de Oliveira ◽  
Maria Cláudia Cabrini Gracio
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Scientific Collaboration ◽  
Methodological Approach ◽  
Scientific Production ◽  
Collaboration Network ◽  
Network Data ◽  
New Knowledge

This paper analyses the scientific collaboration network formed by the Brazilian universities that investigate in dentistry area. The constructed network is based on the published documents in the Scopus (Elsevier) database covering a period of 10 (ten) years. It is used social network analysis as the best methodological approach to visualize the capacity for collaboration, dissemination and transmission of new knowledge among universities. Cohesion and density of the collaboration network is analyzed, as well as the centrality of the universities as key-actors and the occurrence of subgroups within the network. Data were analyzed using the software UCINET and NetDraw. The number of documents published by each university was used as an indicator of its scientific production.

scholarly journals Uma Análise da Produção Técnica e de Colégios Invisíveis com Ênfase nas Patentes Brasileiras

2021 ◽  
Author(s):  
Thiago Magela Rodrigues Dias ◽  
João Vitor de Melo Machado ◽  
Patrícia Mascarenhas Dias
Keyword(s):  
Social Network ◽  
Scientific Collaboration ◽  
Temporal Analysis ◽  
Collaboration Network ◽  
Collaboration Networks ◽  
Collaboration Process ◽  
Network Metrics ◽  
Technical Products ◽  
Identification And Characterization

Analyzes of scientific collaboration networks have been extensively explored in research from different areas of knowledge, in view of their ability to identify how groups of researchers have carried out their work collectively. Such studies make it possible to identify how collaboration between individuals occurs through analyzes based on social network metrics. In this context, new studies have been proposed in order to analyze collaboration in the development of technical products, with data on patents being studied in most studies. This type of analysis is relevant because it makes it possible to understand the collaboration process in the proposal of new inventions. In this work, initially a general characterization of the group of individuals analyzed is presented, and afterwards, a global and temporal analysis of the collaboration network is performed in the proposal of patents of Brazilian individuals with curricula registered in the Lattes Platform. For that, all the patents registered in the curricula of these individuals were used for the identification and characterization of the collaboration networks. As a result, it is possible to see how collaboration in the proposed inventions of the analyzed set has been intensified over the years, with an emphasis on the institutions and areas of expertise of each inventor.

scholarly journals The Evolution of International Scientific Collaboration in Fuel Cells during 1998–2017: A Social Network Perspective

2018 ◽  
Vol 10 (12) ◽  
pp. 4790
Author(s):  
Xuan Shi ◽  
Lingfei Cai ◽  
Junzhi Jia
Keyword(s):  
Fuel Cells ◽  
Social Network ◽  
Fuel Cell ◽  
International Collaboration ◽  
Scientific Collaboration ◽  
Research Collaboration ◽  
Collaboration Network ◽  
Asian Countries ◽  
International Scientific Collaboration ◽  
Wide Range

International scientific collaboration has played an important role in the development of fuel cell technology. In this paper, we employ bibliometric methods and social network analysis to explore the patterns and dynamics of scientific collaboration network of fuel cells. A total of 20,358 international collaborative publications in the fuel cell field published during 1998–2017 were collected from Web of Science. We use a series of indicators to address multiple facets of research collaboration and evolution patterns. Results show that international collaboration has been increasing and the characteristics of the scientific network have changed over time. The collaboration network presented a highly uneven distribution, while the sign of decline began to show. The trend of consolidation was presented with one cluster around North America–Asia, one around Europe, and a small emerging collaborating cluster around West Asia. European and North American countries had relatively higher international collaboration rate than Asian countries but lower publishing volume. Two modes of international collaboration exist: Germany, France and UK collaborate with a wide range of countries, while Singapore, Australia, South Korea and Taiwan concentrate on collaborating with few main countries. Microbial fuel cell had developed as a new prominent area in the international collaboration, and the most popular catalysts were nanoparticle and graphene/carbon nanotubes. This study presents a picture of international collaboration from multi-dimension view and provides insights in facilitating more vigorous collaborations in fuel cells.

scholarly journals Analysis of scientific collaboration network of Italian Institute of Technology

2021 ◽  
Author(s):  
Enrico di Bella ◽  
Luca Gandullia ◽  
Sara Preti
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Scientific Collaboration ◽  
Complex Structure ◽  
Scientific Productivity ◽  
Positive Influence ◽  
Collaboration Network ◽  
Analysis Techniques ◽  
Institute Of Technology ◽  
Over Time

AbstractIt has been proven that collaboration between authors leads to a positive influence on research. This paper aims to analyse the complex structure of the co-authorship network among researchers of the Italian Institute of Technology. In this paper, we examine two different co-authorship networks created starting from the data of the papers published by the Italian Institute of Technology during the period 2006–2019. We apply the main Social Network Analysis techniques to describe the relational structure of the group of researchers and its evolution over time. The structure and characteristics of the networks are analysed both at macro and micro levels, and an attempt is made to identify a possible relationship between the position of researchers in the graphs and their scientific productivity and quality.

scholarly journals Multi-hop assortativities for network classification

2018 ◽  
Vol 7 (4) ◽  
pp. 603-622 ◽  
Author(s):  
Leonardo Gutiérrez-Gómez ◽  
Jean-Charles Delvenne
Keyword(s):  
Machine Learning ◽  
Scientific Collaboration ◽  
State Of The Art ◽  
Medical Engineering ◽  
Research Field ◽  
Classification Task ◽  
Collaboration Network ◽  
Structural Patterns ◽  
Art Methods

Abstract Several social, medical, engineering and biological challenges rely on discovering the functionality of networks from their structure and node metadata, when it is available. For example, in chemoinformatics one might want to detect whether a molecule is toxic based on structure and atomic types, or discover the research field of a scientific collaboration network. Existing techniques rely on counting or measuring structural patterns that are known to show large variations from network to network, such as the number of triangles, or the assortativity of node metadata. We introduce the concept of multi-hop assortativity, that captures the similarity of the nodes situated at the extremities of a randomly selected path of a given length. We show that multi-hop assortativity unifies various existing concepts and offers a versatile family of ‘fingerprints’ to characterize networks. These fingerprints allow in turn to recover the functionalities of a network, with the help of the machine learning toolbox. Our method is evaluated empirically on established social and chemoinformatic network benchmarks. Results reveal that our assortativity based features are competitive providing highly accurate results often outperforming state of the art methods for the network classification task.

scholarly journals Scale free is not rare in international trade networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Linqing Liu ◽  
Mengyun Shen ◽  
Chang Tan
Keyword(s):  
International Trade ◽  
Preferential Attachment ◽  
Developed Countries ◽  
Total Trade ◽  
Trade Networks ◽  
Scale Free ◽  
Weighted Networks ◽  
Free Exploration ◽  
Technology Products ◽  
Attachment Mechanism

AbstractFailing to consider the strong correlations between weights and topological properties in capacity-weighted networks renders test results on the scale-free property unreliable. According to the preferential attachment mechanism, existing high-degree nodes normally attract new nodes. However, in capacity-weighted networks, the weights of existing edges increase as the network grows. We propose an optimized simplification method and apply it to international trade networks. Our study covers more than 1200 product categories annually from 1995 to 2018. We find that, on average, 38%, 38% and 69% of product networks in export, import and total trade are scale-free. Furthermore, the scale-free characteristics differ depending on the technology. Counter to expectations, the exports of high-technology products are distributed worldwide rather than concentrated in a few developed countries. Our research extends the scale-free exploration of capacity-weighted networks and demonstrates that choosing appropriate filtering methods can clarify the properties of complex networks.

Scholar2vec: Vector Representation of Scholars for Lifetime Collaborator Prediction

2021 ◽  
Vol 15 (3) ◽  
pp. 1-19
Author(s):  
Wei Wang ◽  
Feng Xia ◽  
Jian Wu ◽  
Zhiguo Gong ◽  
Hanghang Tong ◽  
...  
Keyword(s):  
Scientific Collaboration ◽  
Early Stage ◽  
Collaboration Network ◽  
Vector Representation ◽  
Network Embedding ◽  
Machine Learning Methods ◽  
Academic Networks ◽  
Special Relationships ◽  
Real World Datasets ◽  
Vector Representations

While scientific collaboration is critical for a scholar, some collaborators can be more significant than others, e.g., lifetime collaborators. It has been shown that lifetime collaborators are more influential on a scholar’s academic performance. However, little research has been done on investigating predicting such special relationships in academic networks. To this end, we propose Scholar2vec, a novel neural network embedding for representing scholar profiles. First, our approach creates scholars’ research interest vector from textual information, such as demographics, research, and influence. After bridging research interests with a collaboration network, vector representations of scholars can be gained with graph learning. Meanwhile, since scholars are occupied with various attributes, we propose to incorporate four types of scholar attributes for learning scholar vectors. Finally, the early-stage similarity sequence based on Scholar2vec is used to predict lifetime collaborators with machine learning methods. Extensive experiments on two real-world datasets show that Scholar2vec outperforms state-of-the-art methods in lifetime collaborator prediction. Our work presents a new way to measure the similarity between two scholars by vector representation, which tackles the knowledge between network embedding and academic relationship mining.

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.

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