Building direct citation networks

2018 ◽  
Vol 115 (2) ◽  
pp. 817-832 ◽  
Author(s):  
Bruno Miranda Henrique ◽  
Vinicius Amorim Sobreiro ◽  
Herbert Kimura
Keyword(s):  
Citation Networks ◽  
Direct Citation

Analysis of direct citation, co-citation and bibliographic coupling in scientific topic identification

2020 ◽  
pp. 016555152096277
Author(s):  
Rajmund Kleminski ◽  
Przemysiaw Kazienko ◽  
Tomasz Kajdanowicz
Keyword(s):  
Citation Network ◽  
Scientific Paper ◽  
Added Value ◽  
Bibliographic Coupling ◽  
Science Literature ◽  
Citation Networks ◽  
Research Topics ◽  
Topic Identification ◽  
Direct Citation ◽  
The Impact

In our study, we examine the impact of citation network structures on the ability to discern valuable research topics in Computer Science literature. We use the bibliographic information available in the DBLP database to extract candidate phrases from scientific paper abstracts. Following that, we construct citation networks based on direct citation, co-citation and bibliographic coupling relationships between the papers. The candidate research topics, in the form of keyphrases and n-grammes, are subsequently ranked and filtered by a graph-text ranking algorithm. This selection of the highest ranked potential topics is further evaluated by domain experts and through the Wikipedia knowledge base. The results obtained from these citation networks are complementary, returning valid but non-overlapping output phrases between some pairs of networks. In particular, bibliographic coupling appears to capture more unique information than either direct citation or co-citation. These findings point towards the possible added value in combining bibliographic coupling analysis with other structures. At the same time, combining direct citation and co-citation is put into question. We expect our findings to be utilised in method design for research topic identification.

scholarly journals Is entrepreneurship an emerging area of research? A computational response

2021 ◽  
Author(s):  
Roney Fraga Souza ◽  
Rosangela Ballini ◽  
José Maria Ferreira Jardim Silveira ◽  
Aurora Amélia Castro Teixeira
Keyword(s):  
Field Research ◽  
Computational Procedure ◽  
Original Version ◽  
Citation Networks ◽  
The Core ◽  
Research Areas ◽  
Formation Method ◽  
Formation And Evolution ◽  
Direct Citation ◽  
Number Of Publications

Objective: We aim to answer four questions. First, with the increasing number of publications, is there a concentration in specific subjects, or on the contrary, a dispersion, amplifying the span of themes related to entrepreneurship? Second, is there a hierarchy of subjects, in the sense that some of them constitute the “core” of entrepreneurship? Third, are they connected with other established research areas? Finally, it is possible to identify papers that are influential, acting as hubs in the cluster’s formation? Method: We developed an original version of the computational procedure proposed by Shibata et al (2008), which allows us to understand the diversity of the different sub-areas of the topic investigated, reducing the need for specialist supervision. Originality / Relevance: We developed and applied a method to capture the formation and evolution of research areas in entrepreneurship literature, via direct citation networks, allowing us to understand the iteration between the different research sub-areas. Results: The dispersion is a feature of entrepreneurship as field research, with a hierarchy between research areas, indicating an emergent organization in the expansion processes. We concluded that research on entrepreneurship consists of specialization, that is, by application in niches.

Models of network formation

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Degree Distribution ◽  
Network Formation ◽  
Preferential Attachment ◽  
Network Size ◽  
Large Network ◽  
Citation Networks ◽  
Airline Networks ◽  
Attachment Model ◽  
Delivery Networks

This chapter describes models of the growth or formation of networks, with a particular focus on preferential attachment models. It starts with a discussion of the classic preferential attachment model for citation networks introduced by Price, including a complete derivation of the degree distribution in the limit of large network size. Subsequent sections introduce the Barabasi-Albert model and various generalized preferential attachment models, including models with addition or removal of extra nodes or edges and models with nonlinear preferential attachment. Also discussed are node copying models and models in which networks are formed by optimization processes, such as delivery networks or airline networks.

scholarly journals Global publication trends and hotspots of molecular biomarkers in DILI from 1991 to 2020: A 30-year bibliometric analysis

2021 ◽  
Vol 104 (1) ◽  
pp. 003685042110005
Author(s):  
Mingnan Cao ◽  
Li Wang ◽  
Lin Zhang ◽  
Jingli Duan
Keyword(s):  
Bibliometric Analysis ◽  
Research Collaboration ◽  
Citation Networks ◽  
Publication Trends ◽  
Drug Induced ◽  
Topic Area ◽  
The Past ◽  
Black Box Warnings ◽  
The Usa ◽  
Post Marketing

Drug-induced liver injury (DILI) is one of the common adverse drug reactions and the leading cause of drug development attritions, black box warnings, and post-marketing withdrawals. Current biomarkers are suboptimal in detecting DILI and predicting its outcome. This study aimed to quantitatively and qualitatively investigate the research trends on DILI biomarkers using bibliometric analysis. All relevant publications were extracted from the Web of Science database. An online analysis platform of literature metrology, bibliographic item co-occurrence matrix builder, and CiteSpace software were used to analyze the publication trends. CitNetExplorer was used to construct direct citation networks and VOSviewer was used to analyze the keywords and research hotspots. We found a total of 485 publications related to DILI biomarkers published from 1991 to 2020. Toxicological Sciences had been the most popular journal in this field over the past 30 years. The USA maintained a top position worldwide and provided a pivotal influence, followed by China. Among all the institutions, the University of Liverpool was regarded as a leader for research collaboration. Moreover, Professors Paul B. Watkins and Tsuyoshi Yokoi made great achievements in topic area. We analyzed the citation networks and keywords, therefore identified five and six research hotspot clusters, respectively. We considered the publication information regarding different countries/regions, organizations, authors, journals, et al. by summarizing the literature on DILI biomarkers over the past 30 years. Notably, the subject of DILI biomarkers is an active area of research. In addition, the investigation and discovery of novel promising biomarkers such as microRNAs, keratin18, and bile acids will be future developing hotspots.

scholarly journals Evolution of interdependent co-authorship and citation networks

2020 ◽  
Vol 125 (1) ◽  
pp. 385-404
Author(s):  
Chakresh Kumar Singh ◽  
Demival Vasques Filho ◽  
Shivakumar Jolad ◽  
Dion R. J. O’Neale
Keyword(s):  
Citation Networks

Research Paper Influence Measurement and Applications: A Machine-Learning-Based Approach

2014 ◽  
Vol 1049-1050 ◽  
pp. 2073-2078
Author(s):  
San Shan Du ◽  
Yue Chun Wu
Keyword(s):  
Machine Learning ◽  
Graph Theory ◽  
Academic Research ◽  
Meaningful Work ◽  
Citation Network ◽  
Citation Networks ◽  
Research Publication ◽  
American Airline ◽  
Airline Network ◽  
Influence Measurement

Measuring the influence of academic research publication is an meaningful work in academe. In this paper, the co-author and the citation networks are built to calculate the influence of a researcher and a paper in the way of networks separately with the discussion of further applications. At the beginning, the co-author network is built to determine the influence of co-authors. Then, based on the citations among the papers in the database, we build up the citation network with the help of graph theory. Thirdly, the method is implemented with the application of American Airline network analysis. As the final, the analysis of strengths and weaknesses is conducted.

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