Use the degree-weighted path count metric to compute a meta-path based similarity score. Node that, in this implementation, type-specific degrees are used (except for the last step of the meta-path).
get_dwpc( x, y, paths_x, paths_y = NULL, reference_list, list_type = c("edge", "neighbor"), verbose = TRUE, w = 0.4 )
x | ID of the origin node. |
---|---|
y | ID of the destination node. |
paths_x | Paths from the origin node following the meta-path of interest as a |
paths_y | Paths from the destination node following the meta-path of interest as a |
reference_list | Either an edge list as a |
list_type | If an edge list is provided, specify |
verbose | Should the intermediate calculations be printed to the console? |
A list with two elements:
The name of the similarity metric (i.e., "Degree-Weighted Path Count"
).
The degree-weighted path count similarity score.
Himmelstein, D. S. & Baranzini, S. E. Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes. PLOS Computational Biology 11, e1004259 (2015).
get_neighbor_list()
for neighbor reference object construction.
Other similarity:
get_npc()
,
get_pathsim()
,
get_pc()