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In the early development of the nleval project, a lightweight graph object was implemented and was used throughout the codebase for the sake of computational and memory efficiencies over networkx. However, there has been a lot of improvement made to the networkx library and it is worth exploring its current state and finding out whether there is a good way to implement backend support using networkx.
Motivation
networkx contains comprehensive features for processing graphs.
networkx is commonly used by many other libraries for, e.g., plotting
The native graph object in nleval is fast, lightweight, and can scale to genome-scale graph efficiently.
Plan
Think of abstraction to allow different graph backend
(add more specifics here)
The text was updated successfully, but these errors were encountered:
In the early development of the
nleval
project, a lightweight graph object was implemented and was used throughout the codebase for the sake of computational and memory efficiencies overnetworkx
. However, there has been a lot of improvement made to thenetworkx
library and it is worth exploring its current state and finding out whether there is a good way to implement backend support usingnetworkx
.Motivation
networkx
contains comprehensive features for processing graphs.networkx
is commonly used by many other libraries for, e.g., plottingnative
graph object innleval
is fast, lightweight, and can scale to genome-scale graph efficiently.Plan
The text was updated successfully, but these errors were encountered: