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

Demo	Data formats	Performance	Data preprocessing
Loading data into StellarGraph from Pandas	Anything supported by Pandas: CSV, TSV, Excel, JSON, SQL, HDF5, many more	Good	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	Best	Via NumPy, scikit-learn and more
Loading data into StellarGraph from NetworkX	Anything supported by NetworkX: Adjacency lists, GEXF, GML, GraphML, Shapefiles, many more	Poor	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.

Table of contents

• Loading data into StellarGraph from NetworkX
• Loading data into StellarGraph from NumPy
• Loading data into StellarGraph from Pandas
• Loading and saving data between StellarGraph and Neo4j