Abstract
One-mode projections of two-mode data are typically valued, and therefore, require dichotomization before they can be analyzed using many network analytic methods. The traditional dichotomization approach, in which a universal threshold is applied to all edge weights, can yield a binary one-mode projection with undesirable artifacts and requires the arbitrary selection of a threshold value. This paper proposes a method and associated Stata command, ONEMODE, for identifying statistically significant edges in one-mode projections, which can be used to construct both binary and signed projections. The method is demonstrated using two-mode data on southern women’s social event participation and US Supreme Court justices’ majority decision participation, and is compared to two alternative approaches for normalizing edge weights in one-mode projections.
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Notes
Throughout this paper, I use the somewhat liberal α = 0.10 for the purposes of illustration; in practice, a more conservative α-level may be more appropriate.
Avoidance of co-participation does not necessarily imply animosity. For example, two friends seeking to break into a social scene might adopt a divide-and-conquer strategy in which they purposefully attend separate social events to maximize exposure.
According to Davis et al. (1941), clique 1 included a core (Evelyn, Laura, Theresa, and Brenda), primary members (Charlotte, Francis, and Eleanor), and a secondary member (Pearl). Likewise, clique 2 included a core (Sylvia, Nora, and Helen), primary members (Myrna and Katherine), and secondary members (Ruth, Verne, Dorothy, Olivia, and Flora).
Comparing these normalizations to the proposed method using the Deep South data yields similar results.
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Neal, Z. Identifying statistically significant edges in one-mode projections. Soc. Netw. Anal. Min. 3, 915–924 (2013). https://doi.org/10.1007/s13278-013-0107-y
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DOI: https://doi.org/10.1007/s13278-013-0107-y