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tno.sdg.graph.gen.graphbin

TNO PET Lab - Synthetic Data Generation (SDG) - Graph - Generation - GraphBin

https://github.com/tno-sdg/graph.gen.graphbin

Science Score: 44.0%

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    Low similarity (16.0%) to scientific vocabulary

Keywords

graph-generation pet-lab sdg synthetic-data-generation tno
Last synced: 6 months ago · JSON representation ·

Repository

TNO PET Lab - Synthetic Data Generation (SDG) - Graph - Generation - GraphBin

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graph-generation pet-lab sdg synthetic-data-generation tno
Created over 2 years ago · Last pushed over 2 years ago
Metadata Files
Readme License Citation

README.md

TNO PET Lab - Synthetic Data Generation (SDG) - Graph - Generation - GraphBin

The TNO PET Lab consists of generic software components, procedures, and functionalities developed and maintained on a regular basis to facilitate and aid in the development of PET solutions. The lab is a cross-project initiative allowing us to integrate and reuse previously developed PET functionalities to boost the development of new protocols and solutions.

The package tno.sdg.graph.gen.graphbin is part of the TNO Python Toolbox.

The research activities that led to this protocol and implementation were supported by TNO's Appl.AI programme.

Limitations in (end-)use: the content of this software package may solely be used for applications that comply with international export control laws.
This implementation of software has not been audited. Use at your own risk.

Documentation

Documentation of the tno.sdg.graph.gen.graphbin package can be found here.

Install

Easily install the tno.sdg.graph.gen.graphbin package using pip:

console $ python -m pip install tno.sdg.graph.gen.graphbin

The package has two groups of optional dependencies:

  • tests: Required packages for running the tests included in this package
  • scripts: The packages required to run the example script

Usage

This repository implements part of the GraphBin algorithm. Currently, the edge generation step of GraphBin is implemented, but not the node generation. It is only supported to generate synthetic graphs "from scratch", i.e. without a source graph from which characteristics are learned. Instead, the current implementation provides the method GraphBin.from_scratch, which generates a new random graph based on the provided parameters.

The parameters are as follows:

  • n_samples: The number of nodes to generate
  • param_feature: Parameter governing exponential distribution from which the value of the "feature" is sampled (i.e. transaction amount)
  • param_degree: Parameter governing the powerlaw distribution from which the degrees of the nodes are sampled
  • cor: Specify the correlation between param_feature and param_degree
  • param_edges: Roughly related to the strength of the binning on the edge probabilities

Below, examples of feature and degree distributions are shown for different values of param_feature and param_degree.

Graph depicting the exponential distribution for various parameters used to sample feature values Graph depicting the powerlaw distribution for various parameters used to sample the degree amounts

Example Script

Be sure to install the scripts optional dependency group (see installation instructions).

```python import matplotlib.pyplot as plt import networkx as nx

from tno.sdg.graph.gen.graphbin import GraphBin

N = 200

graphbin = GraphBin.fromscratch( nsamples=N, paramfeature=2000, paramdegree=19, cor=0.3, paramedges=4000, randomstate=80, ) graph = graphbin.generate() ```

Plot the node degree & node feature.

python plt.figure(figsize=(15, 10), dpi=300) plt.scatter(graph.degree, graph.feature, s=150, alpha=0.65) plt.xlabel("Node degree") plt.ylabel("Node feature") plt.title("Node degree and node feature (node-level feature), for " + str(N) + " nodes") plt.show()

Graph showing the distribution of the degree of the nodes and the feature of the nodes

And the graph:

```python plt.figure(figsize=(15, 10), dpi=300) G = nx.Graph() G.addnodesfrom(graph.index) G.addedgesfrom(tuple(map(tuple, graph.edges)))

pos = nx.springlayout(G, k=100 / N) nx.draw(G, nodesize=350, node_color=graph.feature, pos=pos) plt.title("Synthetic graph with nodes colored by feature value") plt.show() ```

Graph showing the synthetic graph resulting from the example script

Owner

  • Name: TNO - PET Lab - Synthetic Data Generation (SDG)
  • Login: TNO-SDG
  • Kind: organization
  • Email: petlab@tno.nl

Part of TNO PET Lab

Citation (CITATION.cff) 

cff-version: 1.2.0
license: Apache-2.0
message: If you use this software, please cite it using these metadata.
authors:
      - name: TNO PET Lab
        city: The Hague
        country: NL
        email: petlab@tno.nl
        website: https://pet.tno.nl
type: software
url: https://pet.tno.nl
contact:
      - name: TNO PET Lab
        city: The Hague
        country: NL
        email: petlab@tno.nl
        website: https://pet.tno.nl
repository-code: https://github.com/TNO-SDG/graph.gen.graphbin
repository-artifact: https://pypi.org/project/tno.sdg.gen.graph.graphbin
title: TNO PET Lab - Synthetic Data Generation (SDG) - Graph - Generation - GraphBin
version: v0.1.1
date-released: 2023-09-15

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pypi.org: tno.sdg.graph.gen.graphbin

GraphBin: a synthetic graph generator

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 22 Last month
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Dependent packages count: 7.4%
Average: 38.1%
Dependent repos count: 68.9%
Maintainers (1)
PETLab
Last synced: 6 months ago