Identifying hidden coalitions in the US House of Representatives by optimally partitioning signed networks based on generalized balance

Aref, Samin; Neal, Zachary P.

doi:10.1038/s41598-021-98139-w

Cited by 13 publications

(19 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, the network has a high density and large weighted mean degree k = 4688.48 that obscures any structure. Second, the visualization fails to capture the known partisan polarization of the US Senate [5,26,28], which is confirmed by the network's small modularity (Q = 0.18) with respect to political party affiliation. Finally, the network includes as connected two nodes representing Senators Jeff Sessions (R-AL) and Jon Kyl (R-AZ).…”

Section: Empirical Examplementioning

confidence: 97%

“…In political science, the backbone package has been used to infer networks of political alliances among legislators from a bipartite projection of their bill sponsorships [26][27][28][40][41][42]. While two legislators may be viewed as having an alliance when they are observed to sponsor many of the same bills (i.e., the edge weight in a co-sponsorship network), backbone models offer a way to identify pairs of legislators March 22, 2022 19/23 that have sponsored significantly more bills together than expected.…”

Section: Conclusion Applicationsmentioning

confidence: 99%

“…illustrate the extraction of a bipartite projection backbone in practice, we use data on bill sponsorship patterns in the US Senate's 115 th session (2017-2018)[26,27]. In the US Senate, legislators can express support for a bill by 'sponsoring' it.…”

mentioning

confidence: 99%

See 2 more Smart Citations

backbone: An R package to extract network backbones

Neal

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Networks are useful for representing phenomena in a broad range of domains. Although their ability to represent complexity can be a virtue, it is sometimes useful to focus on a simplified network that contains only the most important edges: the backbone. This paper introduces and demonstrates the 'backbone' package for R, which implements methods for extracting the backbone from weighted networks, weighted bipartite projections, and unweighted networks. For each type of network, fully replicable code is presented first for small toy examples, then for complete empirical examples using transportation, political, and social networks. The paper also demonstrates the implications of several issues of statistical inference that arise in backbone extraction. It concludes by briefly reviewing existing applications of backbone extraction using the 'backbone' package, and future directions for research on network backbone extraction.

show abstract

Section: Empirical Examplementioning

confidence: 97%

Section: Conclusion Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

backbone: An R package to extract network backbones

Neal

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…To illustrate the extraction of a bipartite projection backbone in practice, we use data on bill sponsorship patterns in the US Senate’s 115 th session (2017-2018) [ 30 , 31 ]. In the US Senate, legislators can express support for a bill by ‘sponsoring’ it.…”

Section: Backbones Of Bipartite Projectionsmentioning

confidence: 99%

backbone: An R package to extract network backbones

Neal

2022

PLoS ONE

Self Cite

View full text Add to dashboard Cite

Networks are useful for representing phenomena in a broad range of domains. Although their ability to represent complexity can be a virtue, it is sometimes useful to focus on a simplified network that contains only the most important edges: the backbone. This paper introduces and demonstrates a substantially expanded version of the backbone package for R, which now provides methods for extracting backbones from weighted networks, weighted bipartite projections, and unweighted networks. For each type of network, fully replicable code is presented first for small toy examples, then for complete empirical examples using transportation, political, and social networks. The paper also demonstrates the implications of several issues of statistical inference that arise in backbone extraction. It concludes by briefly reviewing existing applications of backbone extraction using the backbone package, and future directions for research on network backbone extraction.

show abstract

“…Adopting such a version of SDSM implies, for example, that in each possible world a given species may be found on many or few islands and a given island may be home to many or few species, but the average number of islands on which a given species lives in all possible worlds and the average number of species that live on an given island in all possible worlds matches these values the observed world. The SDSM has been used to extract the backbone of bipartite projections of, for example, legislators co-sponsoring bills 1 , 18 , 47 – 49 , zebrafish ( Danio rerio ) sharing operational taxonomic units 50 , countries sharing exports 3 , and genes expressed in genesets 51 .…”

Section: Backbone Extraction For Bipartite Projectionsmentioning

confidence: 99%

Comparing alternatives to the fixed degree sequence model for extracting the backbone of bipartite projections

Neal

Domagalski

Sagan

2021

Sci Rep

Self Cite

View full text Add to dashboard Cite

Projections of bipartite or two-mode networks capture co-occurrences, and are used in diverse fields (e.g., ecology, economics, bibliometrics, politics) to represent unipartite networks. A key challenge in analyzing such networks is determining whether an observed number of co-occurrences between two nodes is significant, and therefore whether an edge exists between them. One approach, the fixed degree sequence model (FDSM), evaluates the significance of an edge’s weight by comparison to a null model in which the degree sequences of the original bipartite network are fixed. Although the FDSM is an intuitive null model, it is computationally expensive because it requires Monte Carlo simulation to estimate each edge’s p value, and therefore is impractical for large projections. In this paper, we explore four potential alternatives to FDSM: fixed fill model, fixed row model, fixed column model, and stochastic degree sequence model (SDSM). We compare these models to FDSM in terms of accuracy, speed, statistical power, similarity, and ability to recover known communities. We find that the computationally-fast SDSM offers a statistically conservative but close approximation of the computationally-impractical FDSM under a wide range of conditions, and that it correctly recovers a known community structure even when the signal is weak. Therefore, although each backbone model may have particular applications, we recommend SDSM for extracting the backbone of bipartite projections when FDSM is impractical.

show abstract

Identifying hidden coalitions in the US House of Representatives by optimally partitioning signed networks based on generalized balance

Cited by 13 publications

References 56 publications

backbone: An R package to extract network backbones

backbone: An R package to extract network backbones

backbone: An R package to extract network backbones

Comparing alternatives to the fixed degree sequence model for extracting the backbone of bipartite projections

Contact Info

Product

Resources

About