ORA User's Guide 2013

Carley, Kathleen M.; Pfeffer, Juergen; Reminga, Jeff; Storrick, Jon; Columbus, Dave

doi:10.21236/ada587056

Cited by 56 publications

(42 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nowadays, however, one has a plethora of options, and the user may find it difficult to choose which representation to use. Among others, let us mention: Automap in conjunction with ORA (Carley, Columbus, & Landwehr, 2013;Carley, Pfeffer, Reminga, Storrick, & Columbus, 2013); 2 VOSviewer (van Eck, Waltman, Noyons, & Buter, 2010); 3 Con-Text (Diesner et al, 2015); 4 and Wordjj.exe (Danowski, 2009). 5 Whereas the purpose of the different programs is the same-visualization of the latent structures in the data (Lazarsfeld & Henry, 1968;cf.…”

Section: Introductionmentioning

confidence: 99%

Co‐word maps and topic modeling: A comparison using small and medium‐sized corpora (N < 1,000)

Leydesdorff

Nerghes

2016

Asso for Info Science & Tech

View full text Add to dashboard Cite

Induced by “big data,” “topic modeling” has become an attractive alternative to mapping co‐words in terms of co‐occurrences and co‐absences using network techniques. Does topic modeling provide an alternative for co‐word mapping in research practices using moderately sized document collections? We return to the word/document matrix using first a single text with a strong argument (“The Leiden Manifesto”) and then upscale to a sample of moderate size (n = 687) to study the pros and cons of the two approaches in terms of the resulting possibilities for making semantic maps that can serve an argument. The results from co‐word mapping (using two different routines) versus topic modeling are significantly uncorrelated. Whereas components in the co‐word maps can easily be designated, the topic models provide sets of words that are very differently organized. In these samples, the topic models seem to reveal similarities other than semantic ones (e.g., linguistic ones). In other words, topic modeling does not replace co‐word mapping in small and medium‐sized sets; but the paper leaves open the possibility that topic modeling would work well for the semantic mapping of large sets.

show abstract

Section: Introductionmentioning

confidence: 99%

Co‐word maps and topic modeling: A comparison using small and medium‐sized corpora (N < 1,000)

Leydesdorff

Nerghes

2016

Asso for Info Science & Tech

View full text Add to dashboard Cite

show abstract

“…The idea developed by Graen and Uhl-Bien (1995), whereby dyadic networks develop in concert with task interdependencies, parallels work by Krackhardt and Carley (1998) (Carley, Pfeffer, Reminga, Storrick, & Columbus, 2013), these multiple networks can be evaluated simultaneously to produce a large number of social network indices and statistics. This approach is well-suited for testing the implications of the interplay between dyadic relationship quality and task criticality identified by Graen and Uhl-Bien (1995).…”

Section: Not Just Actors and Ties But Multiple Networkmentioning

confidence: 98%

Tracing Structure, Tie Strength, and Cognitive Networks in LMX Theory and Research

Sparrowe¹,

Emery²,

Bauer³

et al. 2015

Oxford Handbooks Online

View full text Add to dashboard Cite

This chapter reflects on the growing relationship between Leader-Member Exchange (LMX) theory and research and social network analysis. We first discuss the themes of structure and tie strength in relation to several of the theoretical formulations of LMX theory that have served as the foundation for subsequent research. This section proceeds chronologically, beginning with the earliest work on the Vertical Dyad Linkage (as the LMX perspective was initially known) and concluding with recent empirical research integrating LMX and social networks. Our goal is to provide a narrative review of the development of the themes of structure and tie strength within the LMX literature. We then turn to recent developments in the field in which LMX differentiation figures prominently both theoretically and empirically, and engage in a close critical reading of this work from the perspective of cognitive social networks. We conclude by summarizing the opportunities for future research that emerge from our narrative and conceptual analysis.

show abstract

“…Second, from the perspective of trying to bring vulnerable users to the core, articulation points become key nodes upon which to rely on to make this happen. We use UCINET (Borgatti & Everett., 1999) for all analyses, and Organizational Risk Analyzer (Carley et al, 2013) for network visualization. Figure 1 provides a visual representation of the main component of the network 6 .…”

Section: Analytic Strategymentioning

confidence: 99%

Back to the core: A network approach to bolster harm reduction among persons who inject drugs

Bouchard

Hashimi

Tsai

et al. 2018

International Journal of Drug Policy

View full text Add to dashboard Cite

Former or current injecting drug users that were sampled were surrounded by a relatively rich harm reduction network, but the network approach showed that only a minority of individuals were true harm reduction "champions". Recruitment of a combination of well-connected harm reduction champions, and strategically connected articulation points, would be most effective in planning network interventions that encourage harm reduction behaviors among this population.

show abstract

ORA User's Guide 2013

Cited by 56 publications

References 0 publications

Co‐word maps and topic modeling: A comparison using small and medium‐sized corpora (N < 1,000)

Co‐word maps and topic modeling: A comparison using small and medium‐sized corpora (N < 1,000)

Tracing Structure, Tie Strength, and Cognitive Networks in LMX Theory and Research

Back to the core: A network approach to bolster harm reduction among persons who inject drugs

Contact Info

Product

Resources

About