gregori0o / nero-fair_evaluation

Network Embedding based on Relation Order histograms

NERO

Network Embedding based on Relation Order histograms

Installation

While in project main directory (containing the Dockerfile file), run the following command.

docker build -t nero:latest .

Then use the prepared image as a remote Python intepreter. Remember to bind the appropriate container volumes (code and storage locations).

docker run -it --rm --name nero_interpreter -v {project directory path}:/projects/NERO -v {desired storage and output path}:/nero --entrypoint python3 nero /projects/NERO/main.py

Usage

The graph embedding tool can be set up using an object-oriented API.

from nero import constants
from nero.embedding import compressors, digitisers, embedders
from nero.tools import distributions


embedder = embedders.NeroEmbedder(
        bin_generators=[
            digitisers.LabelBinGenerator(
                relevant_property=digitisers.RelevantProperty(
                    name="vertex_label_0", 
                    origin=digitisers.VertexOrigin.NODE,
                ),
            ),
            digitisers.EqualProbabilityBinGenerator(
                relevant_property=digitisers.RelevantProperty(
                    name="edge_attribute_0", 
                    origin=digitisers.VertexOrigin.EDGE,
                ),
                bins_no=20,
                insight_bins_generator=digitisers.InsightBinGenerator(
                    calculate_bins=distributions.equal_size_bins,
                    bins_no=constants.INSIGHT_BINS_NO,
                    bin_type='equal',
                ),
            ),
        ],
        relation_order_compressor=compressors.VGGInspiredROC(
            bins_no=20,
        ),
        jobs_no=8,
    )

It is recommended to use the aforementioned embedder as a part of a scikit-learn processing pipeline.

import sklearn.pipeline as skl_pipeline
import sklearn.preprocessing as skl_preprocessing
import sklearn.ensemble as skl_ensemble

from nero.pipeline.transform import normalise, resize


pipeline = skl_pipeline.Pipeline([
        ('embed', embedder),
        ('resize_to_smallest_common_shape', resize.ToSmallestCommonShape()),
        ('normalise', normalise.PercentOfMaxPerRelationOrder()),
        ('flatten', resize.FlattenUpperTriangles()),
        ('scale', skl_preprocessing.StandardScaler()),
        ('classify', skl_ensemble.ExtraTreesClassifier(
            n_estimators=1000,
            n_jobs=8,
            verbose=False
        )),
    ])

For more detailed usage information check the demonstration example enclosed in the source code and the accompanying conference paper.

BibTeX

@inproceedings{lazarz2021relation,
  title={Relation Order Histograms as a Network Embedding Tool},
  author={{\L}azarz, Rados{\l}aw and Idzik, Micha{\l}},
  booktitle={International Conference on Computational Science},
  pages={224--237},
  year={2021},
  organization={Springer}
}