Incorporating User Feedback into Name Disambiguation of Scientific Cooperation Network

Name disambiguation in scientific cooperation network by exploiting user feedback

Artificial Intelligence Review ◽

10.1007/s10462-012-9323-5 ◽

2012 ◽

Vol 41 (4) ◽

pp. 563-578 ◽

Cited By ~ 11

Author(s):

Yuhua Li ◽

Aiming Wen ◽

Quan Lin ◽

Ruixuan Li ◽

Zhengding Lu

Keyword(s):

User Feedback ◽

Name Disambiguation ◽

Scientific Cooperation ◽

Cooperation Network

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How Does Inter-Organizational Cooperation Impact Organizations’ Scientific Knowledge Generation? Evidence from the Biomass Energy Field

Sustainability ◽

10.3390/su13010191 ◽

2020 ◽

Vol 13 (1) ◽

pp. 191

Author(s):

Liu Li ◽

Chaoying Tang

Keyword(s):

Scientific Knowledge ◽

Biomass Energy ◽

Knowledge Generation ◽

Degree Centrality ◽

Technical Knowledge ◽

Technical Cooperation ◽

Scientific Cooperation ◽

Structural Holes ◽

Cooperation Network ◽

Positive Effect

Previous studies have demonstrated that accessing external knowledge is important for organizations’ knowledge generation. The main purpose of this study is to investigate how the diversity and amount of organizations’ external scientific knowledge influence their scientific knowledge generation. We also consider the moderating effect of the redundant industrial scientific knowledge and the amount of technical knowledge from external technical cooperators. The social network analysis method is used to establish both ego- and industrial-scientific cooperation network, and ego-technical cooperation network in order to analyze the external scientific knowledge and technical knowledge. The empirical analysis is based on patent and article data of 106 organizations in the biomass energy industry (including firms, universities and research institutes), and the results show that organizations’ structural holes and degree centrality of scientific cooperation network have positive effects on their scientific knowledge generation. In addition, organizations’ degree centrality of technical cooperation network positively moderates the relationship between their degree centrality of scientific cooperation network and scientific knowledge generation. Furthermore, density of industrial scientific cooperation network decreases the positive effect of organizations’ structural holes on their scientific knowledge generation, while it strengthens the positive effect of degree centrality of scientific cooperation network on their scientific knowledge generation. Academic contributions and practical suggestions are discussed.

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Clustering-based link prediction in scientific coauthorship networks

International Journal of Modern Physics C ◽

10.1142/s0129183117500826 ◽

2017 ◽

Vol 28 (06) ◽

pp. 1750082 ◽

Cited By ~ 1

Author(s):

Yang Ma ◽

Guangquan Cheng ◽

Zhong Liu ◽

Xingxing Liang

Keyword(s):

Social Networks ◽

Link Prediction ◽

Clustering Method ◽

Scientific Cooperation ◽

Coauthorship Networks ◽

Cooperation Network ◽

Better Than

Link prediction in social networks has become a growing concern among researchers. In this paper, the clustering method was used to exploit the grouping tendency of nodes, and a clustering index (CI) was proposed to predict potential links with characteristics of scientific cooperation network taken into consideration. Results showed that CI performed better than the traditional indices for scientific coauthorship networks by compensating for their disadvantages. Compared with traditional algorithms, this method for a specific type of network can better reflect the features of the network and achieve more accurate predictions.

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Analysis of an Optimal Measurement Index Based on the Complex Network

Cybernetics and Information Technologies ◽

10.1515/cait-2016-0058 ◽

2016 ◽

Vol 16 (5) ◽

pp. 119-126

Author(s):

Hua Guan ◽

Zhen Zhao ◽

Lu Dai

Keyword(s):

Network Analysis ◽

Complex Network ◽

Data Sets ◽

Network Node ◽

Cooperative Network ◽

Scientific Cooperation ◽

Node Cooperation ◽

Index Analysis ◽

Cooperation Network ◽

Weighted Measure

Abstract In this paper, the discussion on the scientific cooperation network structure, the use of complex network analysis and social network analysis and the network analysis result from a scientific cooperation in both dynamic and static perspective. This paper extracts articles from 1995 to 2015 in conference proceedings, as experimental data sets with the corresponding network are called data mining cooperative network. The classic center-based index analysis was proposed by an improved node center metrics (c-index), weighted measure of the power of collaborative network node cooperation.

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Acinetobacter baumannii: Researchers' Scientific Cooperation Network in Three Decades

Iranian Journal of Medical Microbiology ◽

10.30699/ijmm.14.3.252 ◽

2020 ◽

Vol 14 (3) ◽

pp. 252-269

Author(s):

Farshid Danesh ◽

Somayeh Ghavidel ◽

Maryam Emami ◽

◽

...

Keyword(s):

Acinetobacter Baumannii ◽

Scientific Cooperation ◽

Cooperation Network

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The Scientific Cooperation Network of Chinese Scientists and Its Proximity Mechanism

Sustainability ◽

10.3390/su12020660 ◽

2020 ◽

Vol 12 (2) ◽

pp. 660 ◽

Cited By ~ 1

Author(s):

Wentian Shi ◽

Wenlong Yang ◽

Debin Du

Keyword(s):

Negative Binomial ◽

Scientific Research ◽

Negative Binomial Regression ◽

Scientific Development ◽

Negative Binomial Regression Model ◽

China National Knowledge Infrastructure ◽

Scientific Cooperation ◽

Research Activities ◽

Binomial Regression ◽

Cooperation Network

The collaboration of scientists is important for promoting the scientific development and technological progress of a country, and even of the world. Based on the cooperation data of academicians of the Chinese Academy of Sciences (CAS) in the China National Knowledge Infrastructure (CNKI), we portray the scientific cooperation network of Chinese scientists using Pajek, Gephi, ArcGIS, and other software, and the complexity of the scientific cooperation network of Chinese scientists and its proximity mechanism are explored by combining complex network analysis, spatial statistical analysis, and negative binomial regression models. Our main conclusions are as follows: (1) In terms of network structure, the scientific cooperation network of Chinese scientists has a multi-triangular skeleton, with Beijing as its apex. The network has an obvious hierarchical structure. Beijing and Shanghai are located in the core area, and 16 cities are located in the semi-periphery of the network, while other cities are located at the periphery of the network. (2) In terms of spatial distribution, the regional imbalance of the scientific cooperation of Chinese scientists is obvious. Beijing–Tianjin–Hebei, the Yangtze River Delta, and the central-south region of Liaoning are hot spots for the scientific research activities of Chinese scientists. (3) The negative binomial regression model accurately explains the proximity mechanism of the scientific cooperation network of Chinese scientists. The geographical proximity positively affects the scientific cooperation of Chinese scientists under certain conditions. The educational proximity is the primary consideration for scientists to cooperate in scientific research. The closer the educational level of the cities, the greater the cooperation. Economic and social proximity can promote scientific cooperation among scientists, whereas institutional proximity negatively and significantly affects scientific cooperation.

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