Eigenvector centrality and its application in research professionals' relationship network

Bihari, Anand; Pandia, Manoj Kumar

doi:10.1109/ablaze.2015.7154915

Cited by 34 publications

(13 citation statements)

References 14 publications

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“…We used Freeman's degree to measure the total number of a publication's co-citations in the database (Freeman 1978). The eigenvector centrality represents an extension of the degree centrality by considering the importance of articles to which a publication is related (Bihari and Pandia 2015). The eigenvector score, which also underlies Google's page ranking (Langville and Meyer 2006), reflects various publications' centrality in the entire research field better than unweighted measures do.…”

Section: Discussionmentioning

confidence: 99%

Research streams in corporate social responsibility literature: a bibliometric analysis

Frerichs

Teichert

2021

Manag Rev Q

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Corporate social responsibility (CSR) research is heterogeneous and still fragmented. In its interdisciplinary setting, researchers focus on different CSR aspects, secondary concepts and themes. The lack of a unifying paradigm indicates that the CSR literature should be summarized and classified. This study’s systematic overview of CSR research provides such a classification. Previous conceptualizations of CSR research mapped the literature from individual authors’ perspective, rendering different and partly inconsistent classifications. Using bibliometric methods, this paper offers an objective overview. We analyze the references of 1902 CSR journal articles by bibliometric techniques as (co-)citation, core/periphery, factor, and network analyses. By doing that, we provide an overview of the CSR research core, identify different research streams, describe their main publications’ topics and recent developments, and make suggestions to inspire future research in and across research streams. Our results show the increased relevance of formerly niche research streams, such as employee-oriented CSR research, or research on consumer skepticism. Among others, process-oriented and micro-level research, critical approaches, and mergers between themes from various research fields offer a wide scope for further research.

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Section: Discussionmentioning

confidence: 99%

Research streams in corporate social responsibility literature: a bibliometric analysis

Frerichs

Teichert

2021

Manag Rev Q

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“…A cluster analysis was conducted to classify subgroups that were closely connected to each other in the networks using the Clauset-Newman-Moore cluster algorithm (Clauset et al, 2004). Eigenvector centrality index was also computed that gauges the influence of a node in a network by assessing relative ranking of a node based on its connection to central nodes in a network (Bihari & Pandia, 2015). In addition, the topography of the networks was visualized using the Harel-Koren Fast Multiscale layout algorithm from NodeXL.…”

Section: Social and Semantic Network Analysismentioning

confidence: 99%

Infodemiological Study on the Use of Face Masks during Covid-19: Comparing U.S. and Korea

Choi

Park

2021

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In the midst of the COVID-19 pandemic, there have been varied responses to public health officials' recommendations about wearing face masks as a means to slow the spread of the virus. This study, by using Twitter data, aims to explore the role of digital technology in facilitating public conversations and formulating public perception regarding face masks during the COVID-19 pandemic in two contrasting contexts: the U.S. and South Korea. From January 1, 2020 to April 14, 2020, a total of 22,928 users generated 27,501 tweets regarding face masks in the U.S. network, whereas 17,267 users produced 18,686 tweets in that of South Korea. The results of the semantic network analysis shed light on Americans' initial resistance to wearing masks as well as Koreans' willingness to comply. Details of the results are discussed further in the paper. With real-time data aggregation, this study gives insight into the rising controversy regarding wearing face masks during COVID-19 while providing implications for health officials designing strategic communication messages.

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“…Power method is used for evaluation of EVC from of the network. For this method, we initiate from the ones vector that is [ ] corresponding to the count of nodes in the network and passes through a number of iterations [2,9,10]. The preliminary eigenvector evaluated during the( ) iteration is given as follows:…”

Section: Eigenvector Centrality (Evc)mentioning

confidence: 99%

Efficient Algorithm for Maximal Clique Size Evaluation

Fatima¹,

Hina²

2019

IJACSA

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A large dataset network is considered for computation of maximal clique size (MC). Additionally, its link with popular centrality metrics to decrease uncertainty and complexity and for finding influential points of any network has also been investigated. Previous studies focus on centrality metrics like degree centrality (DC), closeness centrality (CC), betweenness centrality (BC) and Eigenvector centrality (EVC) and compare them with maximal clique size however, in this study Katz centrality measure is also considered and shows a pretty robust relation with maximal clique size (MC). Secondly, maximal clique size (MC) algorithm is also revised for network analysis to avoid complexity in computation. Association between MC and five centrality metrics has been evaluated through recognized methods that are Pearson's correlation coefficient (PCC), Spearman's correlation coefficient (SCC) and Kendall's correlation coefficient (KCC). The strong strength of association between them is seen through all three correlation coefficients measure.

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Eigenvector centrality and its application in research professionals' relationship network

Cited by 34 publications

References 14 publications

Research streams in corporate social responsibility literature: a bibliometric analysis

Research streams in corporate social responsibility literature: a bibliometric analysis

Infodemiological Study on the Use of Face Masks during Covid-19: Comparing U.S. and Korea

Efficient Algorithm for Maximal Clique Size Evaluation

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