Applying centrality measures to impact analysis: A coauthorship network analysis

Yan, Erjia; Ding, Ying

doi:10.1002/asi.21128

Cited by 346 publications

(295 citation statements)

References 63 publications

(102 reference statements)

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“…Again, the highest ranked are Spink, Marchianoni and Belkin. As noted by Yan and Ding (2009), they are the authors who collaborate the most frequently (degree), widely (closeness), and diversely (betweenness), and are therefore those with the greatest influence. Other authors who rank highly in degree are those who simply have more direct collaborations.…”

Section: Discussionmentioning

confidence: 99%

“…There is not, however, a consensus on the exact importance of each of these measures. They can be related to a greater amount of productivity (Yan and Ding 2009;Badar et al 2013), or could be evidence that they are ''links of preference'' when incorporating new authors (Abbasi et al 2012). These factors could serve to advance the development of the co-authorship network, as two authors not linked, but with contacts in common, may collaborate in the future, although it is unlikely that those with a large number of intermediaries would do so.…”

Section: Discussionmentioning

confidence: 99%

“…Degree is the number of ties that a vertex has to other vertices in its network. Yan and Ding (2009) relate this to the ability of an actor to influence others. Closeness refers to the speed with which an actor can interact with other actors in the network, either directly or indirectly through intermediaries.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Mapping recent information behavior research: an analysis of co-authorship and co-citation networks

et al. 2015

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There has been an increase in research published on information behavior in recent years, and this has been accompanied by an increase in its diversity and interaction with other fields, particularly information retrieval. The aims of this study are to determine which researchers have contributed to producing the current body of knowledge on this subject, and to describe its intellectual basis. A bibliometric and network analysis was applied to authorship and co-authorship as well as citation and co-citation. According to these analyses, there is a small number of authors who can be considered to be the most productive and who publish regularly, and a large number of transient ones. Other findings reveal a marked predominance of theoretical works, some examples of qualitative methodology that originate in other areas of social science, and a high incidence of research focused on the user interaction with information retrieval systems and the information behavior of doctors.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Mapping recent information behavior research: an analysis of co-authorship and co-citation networks

et al. 2015

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“…Betweenness centrality ( ) [43,51] is constructed on the number of shortest paths passing over a node. It is assumed that the node with a high betweenness have the significance position of linking different communities.…”

Section: Influential Spreaders Identification Algorithms In Complex Nmentioning

confidence: 99%

Identifying the influential spreaders in multilayer interactions of online social networks

Al-Garadi

Varathan

Ravana

et al. 2016

IFS

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Abstract. Online social networks (OSNs) portray a multi-layer of interactions through which users become a friend, information is propagated, ideas are shared, and interaction is constructed within an OSN. Identifying the most influential spreaders in a network is a significant step towards improving the use of existing resources to speed up the spread of information for application such as viral marketing or hindering the spread of information for application like virus blocking and rumor restraint. Users communications facilitated by OSNs could confront the temporal and spatial limitations of traditional communications in an exceptional way, thereby presenting new layers of social interactions, which coincides and collaborates with current interaction layers to redefine the multiplex OSN. In this paper, the effects of different topological network structure on influential spreaders identification are investigated. The results analysis concluded that improving the accuracy of influential spreaders identification in OSNs is not only by improving identification algorithms but also by developing a network topology that represents the information diffusion well. Moreover, in this paper a topological representation for an OSN is proposed which takes into accounts both multilayers interactions as well as overlaying links as weight. The measurement results are found to be more reliable when the identification algorithms are applied to proposed topological representation compared when these algorithms are applied to single layer representations.

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“…Nor do we consider indices accounting for the quality of the citations in terms of the collaboration distance between citing and cited authors [47], as we do not model the presence of research groups. Finally, we do not consider metrics based on the eigenvector centrality within the citation network [48], such as PageRank [49], CiteRank [49], or PhysAuthorRank [50]. This is because the linking probability defined by Eq.…”

Section: Impact Indicatorsmentioning

confidence: 99%

Model-based evaluation of scientific impact indicators

Medo

Cimini

2016

Phys. Rev. E

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Using bibliometric data artificially generated through a model of citation dynamics calibrated on empirical data, we compare several indicators for the scientific impact of individual researchers. The use of such a controlled setup has the advantage of avoiding the biases present in real databases, and it allows us to assess which aspects of the model dynamics and which traits of individual researchers a particular indicator actually reflects. We find that the simple average citation count of the authored papers performs well in capturing the intrinsic scientific ability of researchers, regardless of the length of their career. On the other hand, when productivity complements ability in the evaluation process, the notorious h and g indices reveal their potential, yet their normalized variants do not always yield a fair comparison between researchers at different career stages. Notably, the use of logarithmic units for citation counts allows us to build simple indicators with performance equal to that of h and g. Our analysis may provide useful hints for a proper use of bibliometric indicators. Additionally, our framework can be extended by including other aspects of the scientific production process and citation dynamics, with the potential to become a standard tool for the assessment of impact metrics.

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Applying centrality measures to impact analysis: A coauthorship network analysis

Cited by 346 publications

References 63 publications

Mapping recent information behavior research: an analysis of co-authorship and co-citation networks

Mapping recent information behavior research: an analysis of co-authorship and co-citation networks

Identifying the influential spreaders in multilayer interactions of online social networks

Model-based evaluation of scientific impact indicators

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