Python has proven to be a popular choice for data scientists in
the domain of graph analytics. The multitude of freely available
frameworks and python packages allow to develop applications
quickly through ease of expressibility and reuse of code.
With petabytes of data generated everyday and an ever evolving
landscape of hardware solutions, we observe a graph processing
framework should expose the following characteristics: ease of
use, scalability, interoperability across data formats, and
portability across hardware vendors.
While existing python packages have been helping to drive
application development, our assessment is that none of the
packages address all the aforementioned challenges.
We propose a community led, open source effort, to design and
build a graph processing python library to specifically address
these challenges.