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mercury-graph

mercury-graph is a Python library that offers graph analytics capabilities with a technology-agnostic API, enabling users to apply a curated range of performant and scalable algorithms and utilities regardless of the underlying data framework. The consistent, scikit-like interface abstracts away the complexities of internal transformations, allowing users to effortlessly switch between different graph representations to leverage optimized algorithms implemented using pure Python, numba, networkx and PySpark GraphFrames.

Currently implemented submodules in mercury.graph include:

  • mercury.graph.core, with the main classes of the library that create and store the graphs' data and properties.

  • mercury.graph.ml, with graph theory and machine learning algorithms such as Louvain community detection, spectral clustering, Markov chains, spreading activation-based diffusion models and graph random walkers.

  • mercury.graph.embeddings, with classes that calculate graph embeddings in different ways, such as following the Node2Vec algorithm.

  • mercury.graph.viz, with capabilities for graph visualization.

Repository

The website for the GitHub repository can be found here.