Bipartite (two-mode) networks are ubiquitous. When calculating node centrality measures in bipartite networks, a common approach is to apply PageRank on the one-mode projection of the network. However, the projection can cause information loss and distort the network topology. For better node ranking on bipartite networks, it is preferable to use a ranking algorithm that fully accounts for the topology of both modes of the network.
We present the BiRank package, which implements bipartite ranking algorithms HITS, CoHITS, BGRM, and BiRank. BiRank provides convenience options for incorporating node-level weights into rank estimations, allowing maximum flexibility for different purpose. It can efficiently handle networks with millions of nodes on a single midrange server. Both R and Python versions are available.
CRAN package with highly efficient functions for estimating various rank (centrality) measures of nodes in bipartite graphs (two-mode networks) including HITS, CoHITS, BGRM, and BiRank. Also provides easy-to-use tools for incorporating or removing edge-weights during rank estimation, projecting two-mode graphs to one-mode, efficiently estimating PageRank in one-mode graphs, and for converting edgelists and matrices to sparseMatrix format. Best of all, the package's rank estimators can work directly with common formats of network data including edgelists (class
tbl_df) and adjacency matrices (class
This package can be directly installed via CRAN with
install.packages("birankr"). Alternatively, newest versions of this package can be installed with
Let's pretend we have a dataset (
df) containing patient-provider ties (
provider_id) among providers that have ever prescribed an opioid:
df <- data.frame( patient_id = sample(x = 1:10000, size = 10000, replace = T), provider_id = sample(x = 1:5000, size = 10000, replace = T) )
We are interested in identifying patients who are likely doctor shopping. We assume that a highly central patient in the patient-doctor network is likely to be a person who is deliberately identifying more "generous" opioid prescribers. We therefore estimate a patients' rank in this network with the CoHITS algorithm:
df.rank <- br_cohits(data = df)
Note that rank estimates are scaled according to the size of the network, with more nodes tending to result in smaller ranks. Due to this, it is often advisable to rescale rank estimates more interpretable numbers. For example, we could rescale such that the mean rank = 1 with the following data.table syntax:
df.rank <- data.table(df.rank) df.rank[, rank := rank/mean(rank)]
Finally, we decide to identify the IDs and ranks of the highest ranking patients in
head(df.rank[order(rank, decreasing = T), ], 10)
For a more detailed example, check out examples/Marvel_social_network.md, where we use the ranking algorithm to analyze the Marvel comic book social network.
Below is a brief outline of each function in this package:
Full documentation of
birankr can be found in birankr.pdf.
birankpy provides functions for estimating various rank measures of nodes in bipartite networks including HITS, CoHITS, BGRM, and BiRank.
It can also project two-mode networks to one-mode, and estimate PageRank on it.
birankpy allows user-defined edge weights.
Implemented with sparse matrix, it's highly efficient.
pip install birankpy
Let's pretend we have an edge list
edgelist_df containing ties between top nodes and bottom nodes:
top_node | bottom_node ------------ | ------------- 1 | a 1 | b 2 | a ...|.. 123| z
To performing BiRank on this bipartite network, just:
bn = birankpy.BipartiteNetwork() bn.set_edgelist(edgelist_df, top_col='top_node', bottom_col='bottom_node') top_birank_df, bottom_birank_df = bn.generate_birank()
For a more detailed example, check out examples/Marvel_social_network.ipynb, where we use the ranking algorithm to analyze the Marvel comic book social network.
See documentation for
birankpy at birankpy doc.
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