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
- 1.
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.
- 2.
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.
- 3.
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).
- 4.
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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Keywords
- Bipartite
- Dichotomize
- One-mode projection
- Signed network
- Two-mode