Centre for Research & Technology Hellas, Information Technologies Institute
We introduce pygrank, an open-source Python package to define, run and evaluate node ranking algorithms. We provide object-oriented and extensively unit-tested algorithm components, such as graph filters, post-processors, measures, benchmarks and online tuning.
Computations can be delegated to numpy, tensorflow or pytorch backends and fit in backpropagation pipelines. Classes can be combined to define interoperable complex algorithms.
Within the context of this paper we compare the package with related alternatives and demonstrate its flexibility and ease of use with code examples.
Keywords: graph signal processing, node ranking, optimization