Flexible Framework for Graph Feature Engineering
GraphFlex is a modular and extensible framework for graph-based feature engineering in Python. It allows seamless integration of graph datasets with traditional machine learning pipelines using familiar tools like scikit-learn.
🔗 Homepage & Documentation: GraphFlex on GitHub
pip install graphflexGraphFlex supports several optional extras. Install them with:
pip install "graphflex[dgl]"
pip install "graphflex[neo4j]"
pip install "graphflex[rdflib]"
pip install "graphflex[full]" # all optional features# GraphFlex pipeline
from graphflex import GraphFlex
from graphflex.functions.postprocessing.filter import NonUniqueFeatureFilter
from graphflex.functions.feature import MeanStdFeature
from graphflex.functions.edgenode import NumericalEdgeNode
connect = Connector(...) #use defined connector here
gflex = GraphFlex(
connector=connect,
node_feature=MeanStdFeature(),
edge_node_feature=NumericalEdgeNode(),
post_processor=NonUniqueFeatureFilter()
)
nodes = ...
feature_matrix = gflex.fit_transform(nodes)- Plug-and-play feature extraction for graph nodes
- Compatible with
scikit-learnpipelines - Support for multiple graph backends (DGL, RDFLib-HDT, Neo4j, ...)
- Built-in feature functions and postprocessing modules
- Easily extendable with custom logic
For full documentation, examples, and API reference, visit the GraphFlex repository.
- Python ≥ 3.10
numpy,pandas,scikit-learn,tqdm- Optional:
dgl,torch,torchdata,rdflib-hdt,neo4j,PyYAML,pydantic
Bram Steenwinckel – bram.steenwinckel@ugent.be
This project is licensed under the MIT License.