Getting Started with bluegraph

The examples directory contains a set of Jupyter notebooks providing tutorials and usecases for BlueGraph.

To get started with property graph data structure PGFrame provided by BlueGraph, get an example of semantic property encoding, see Intro to PGFrames and semantic encoding (notebook).

To get familiar with the ideas behind the co-occurrence analysis and the graph analytics interface provided by BlueGraph we recommend to have a look at the following tutorials:

• Literature exploration: in-memory analytics tutorial illustrates how to use BlueGraphs’s analytics API for in-memory graph backends based on the NetworkX and the graph-tool libraries (notebook).

• NASA dataset keywords analysis: Neo4j analytics tutorial illustrates how to use the Neo4j-based analytics API for persistent property graphs (notebook).

• Embedding and downstream tasks tutorial starts from the co-occurrence graph generation example and guides the user through the graph representation learning and all it’s downstream tasks including node similarity queries, node classification, edge prediction and embedding pipeline building (notebook).