Trends in science networks: understanding structures and statistics of scientific networks

Kas, Miray; Carley, Kathleen M.; Carley, L.R.

doi:10.1007/s13278-011-0044-6

Cited by 36 publications

(27 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Our method differs from theirs, however, because we restrict our analysis to finding permanent user communities that share similar interests over a given time. Kas et al (2012) also discuss the topology changes in social networks based on changing trends and temporal group evolution.…”

Section: Motivationmentioning

confidence: 99%

Characterizing Dynamic Group Behavior in Social Networks for Cybernetics

Dua¹,

Chowriappa²

2014

Data Analysis for Network Cyber-Security

View full text Add to dashboard Cite

This chapter is an attempt to characterize users sentiments or opinions for the creation of ad hoc communities over social networks for effective situational awareness. We believe that the mined patterns in user opinions can act as indicators of potential threats in cyberspace. This chapter proposes a novel data-mining approach to identify ad hoc communities and track these communities to effectively identify users and topics that influence the dynamics of a community over time. IntroductionThe recent excrescence of social networking (SN) and social media (SM) as a medium of communication cannot be overlooked primarily for its farreaching applications and outreach. Both SN and SM have developed from a means of casual communication to "virtual glue" that connects individuals over cyberspace. This constantly evolving and dynamic cyber system provides a deluge of data and information that can be exploited to enhance the situational awareness (SA). Existing techniques to achieve SA work by modeling the structure of user communities. These techniques employ standard visualization and quantitative tools to measure correlations between profiles of users within a community and users across communities. Though SA in SN lacks a clear definition and the use of a social network-based SA system is still in the conceptual form, this chapter is aimed at exploiting SN for effective SA. * corresponding author. 105Data Analysis for Network Cyber-Security Downloaded from www.worldscientific.com by NANYANG TECHNOLOGICAL UNIVERSITY on 10/01/15. For personal use only.

show abstract

Section: Motivationmentioning

confidence: 99%

Characterizing Dynamic Group Behavior in Social Networks for Cybernetics

Dua¹,

Chowriappa²

2014

Data Analysis for Network Cyber-Security

View full text Add to dashboard Cite

show abstract

“…In other words, a paper that has already been cited by many papers holds a visibility advantage over other less-cited papers, leading to the emergence of power laws [14].…”

Section: Popularity and Prestige In Sciencementioning

confidence: 99%

“…Kas, Carley & Carley also explain recency bias on citation as the tendency of authors for citing recent papers; even highly cited papers stop receiving citations over time [14].…”

Section: Temporal Geographical and Social Closenessmentioning

confidence: 99%

“…Kas, Carley & Carley explained that this concept, also known as transitivity, is a frequent phenomenon of social networks [14]. In co-authorship, the triangular closure implies that authors develop relationships with existing co-authors of their immediate contacts or closest inner circle.…”

Section: Similar and Reachable Collaboratorsmentioning

confidence: 99%

“…Specifically co-authorship is the most visible and accessible indicator of scientific collaboration [19]. These studies correlate scientist attributes and their position in the co-authorship network with high scientific productivity [2,21,14,11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Recommending intra-institutional scientific collaboration through coauthorship network visualization

Parada

Ceballos

Cantú

et al. 2013

Proceedings of the 2013 Workshop on Computational Scientometrics: Theory &Amp; Applications - CompSci '13

View full text Add to dashboard Cite

For improving research productivity, quality and dissemination, we propose the development of a visual recommendation tool summing up scientific collaboration best-practices found in literature. Social Network Analysis are applied to a coauthorship network for generating a Potential Collaboration Index (PCI) based on productivity, connectivity, similarity and expertise. This work is evaluated by recommending intra-institutional collaboration in a comprehensive university. The accuracy of PCI is documented, along with suggestions and comments from 27 interviewed researchers.

show abstract

Impact of medical academic genealogy on publication patterns: An analysis of the literature for surgical resection in brain tumor patients

Hirshman

Tang

Jones

et al. 2016

Annals of Neurology

Self Cite

View full text Add to dashboard Cite

"Academic genealogy" refers to the linking of scientists and scholars based on their dissertation supervisors. We propose that this concept can be applied to medical training and that this "medical academic genealogy" may influence the landscape of the peer-reviewed literature. We performed a comprehensive PubMed search to identify US authors who have contributed peer-reviewed articles on a neurosurgery topic that remains controversial: the value of maximal resection for high-grade gliomas (HGGs). Training information for each key author (defined as the first or last author of an article) was collected (eg, author's medical school, residency, and fellowship training). Authors were recursively linked to faculty mentors to form genealogies. Correlations between genealogy and publication result were examined. Our search identified 108 articles with 160 unique key authors. Authors who were members of 2 genealogies (14% of key authors) contributed to 38% of all articles. If an article contained an authorship contribution from the first genealogy, its results were more likely to support maximal resection (log odds ratio = 2.74, p < 0.028) relative to articles without such contribution. In contrast, if an article contained an authorship contribution from the second genealogy, it was less likely to support maximal resection (log odds ratio = -1.74, p < 0.026). We conclude that the literature on surgical resection for HGGs is influenced by medical academic genealogies, and that articles contributed by authors of select genealogies share common results. These findings have important implications for the interpretation of scientific literature, design of medical training, and health care policy.

show abstract

Trends in science networks: understanding structures and statistics of scientific networks

Cited by 36 publications

References 43 publications

Characterizing Dynamic Group Behavior in Social Networks for Cybernetics

Characterizing Dynamic Group Behavior in Social Networks for Cybernetics

Recommending intra-institutional scientific collaboration through coauthorship network visualization

Impact of medical academic genealogy on publication patterns: An analysis of the literature for surgical resection in brain tumor patients

Contact Info

Product

Resources

About