StellarGraph basics

StellarGraph has support for loading data via Pandas, NetworkX and Neo4j. This folder contains examples of the loading data into a StellarGraph object, which is the format used by the machine learning algorithms in this library.

Find demos for a format


Data formats


Data preprocessing

Loading data into StellarGraph from Pandas

Anything supported by Pandas: CSV, TSV, Excel, JSON, SQL, HDF5, many more


Via Pandas, scikit-learn and more

Loading data into StellarGraph from NumPy

Anything supported by NumPy, SciPy or other libraries: CSV, TSV, MATLAB .mat, NetCDF, many more


Via NumPy, scikit-learn and more

Loading data into StellarGraph from NetworkX

Anything supported by NetworkX: Adjacency lists, GEXF, GML, GraphML, Shapefiles, many more


Via graph-focused transforms and functions in NetworkX

Loading and saving data between StellarGraph and Neo4j

Any Cypher query supported by Neo4j

Good for subgraphs and other queries

Via Cypher functionality

See all demos for machine learning algorithms.