This paper describes how to use the R package backbone for extracting the backbone of a weighted graph. The analysis and visualization of weighted graphs are inherently challenging , and thus it is often of use to only examine edges which are deemed significant. There are several methods for extracting from a weighted graph an unweighted or signed backbone which retains only the significant edges. The backbone package implements four different backbone methods, three of which consider weighted graphs that arise from bipartite projections. This paper describes how to use each of the package's functions and the algorithms they employ .
Bipartite projections are used in a wide range of network contexts including politics (bill co-sponsorship), genetics (gene co-expression), economics (executive board co-membership), and innovation (patent co-authorship). However, because bipartite projections are always weighted graphs, which are inherently challenging to analyze and visualize, it is often useful to examine the 'backbone', an unweighted subgraph containing only the most significant edges. In this paper, we introduce the R package backbone for extracting the backbone of weighted bipartite projections, and use bill sponsorship data from the 114th session of the United States Senate to demonstrate its functionality .
R
This paper describes how to use the R package backbone for extracting the backbone of a weighted graph. The analysis and visualization of weighted graphs
Bipartite projections are used in a wide range of network contexts including politics (bill co-sponsorship), genetics (gene co-expression), economics (executive board co-membership), and innovation (patent co-authorship). However, because bipartite projections are always weighted graphs, which
R
, and thus
to analyze and visualize,
R
of use to only examine edges which are deemed significant. There are several methods for extracting from a weighted graph an unweighted or signed backbone which retains only the
useful to examine the 'backbone', an unweighted subgraph containing only the most
R
The backbone package implements four different backbone methods, three of which consider weighted graphs that arise from bipartite projections. This paper describes how to use each of the package's functions and the algorithms they employ
In this paper, we introduce the R package backbone for extracting the backbone of weighted bipartite projections, and use bill sponsorship data from the 114th session of the United States Senate to demonstrate its functionality