Repo containing MPhil thesis code for link prediction in supply chain knowledge graphs using Graph Representation Learning.
You can get started with recreating the GNN analysis by running
pip install -r requirements.txt followed by running the training and testing scripts with:
python3 main.py --cpu (alternatively --gpu if you are lucky enough to have one 🌝)
Modern supply chains lend themselves to a KG representation based on rich metadata regarding their certifications, location, buying and selling relationships, and capabilities. A KG representation allows for companies to interrogate their supply chains in a novel way. Examples including finding alternative suppliers, building relationships (or removing relationships in nefarious instances). The following image is an extrac of the KG built for an automotive suppply chain.
The ontology of the graph is given as:
| Nodes | Number |
|---|---|
| company (e.g. General Motors) | 119,599 |
| product (e.g. Floor mat) | 119,618 |
| capability (e.g. Machining) | 36 |
| certification (e.g. ISO9001) | 9 |
Edges in the ontology
| Edges | Number |
|---|---|
| ('capability', 'capability_produces', 'product') | 21,857 |
| ('company', 'buys_from', 'company') | 88,997 |
| ('company', 'has_capability', 'capability') | 83,787 |
| ('company', 'has_cert', 'certification') | 32,654 |
| ('company', 'located_in', 'country') | 40,421 |
| ('company', 'makes_product', 'product') | 119,618 |
| ('product', 'complimentary_product_to', 'product') | 260,658 |
If you find this implementation useful, please consider citing the following article.
@article{
author = {Aziz, Ajmal and Kosasih, Edward and Griffiths, Ryan-Rhys and Brintrup, Alexandra},
journal = {International Conference for Machine Learning (ICML) workshop on ML4Data},
year = {2021},
month = {07},
title = {Data Considerations in Graph Representation Learning for Supply Chain Networks}
}
The file structure is laid out as follows:
├── README.md # The top-level README
├── config # 🏃🏻 Run Project configurations
│ ├── config.yml # For changing run parameters (e.g. number of epochs 🌝)
│ └── sweep_config.yml
├── data # 👀 Ask for GDrive Access
│ ├── 01_raw # 💾 Data from third party sources.
│ │ ├── raw_df.pkl
│ │ └── supplier_product_df.parquet
│ ├── 02_intermediate # 💪🏼 Intermediate data that has been transformed.
│ │ ├── G.pickle
│ │ ├── bG.pickle
│ │ ├── cG.pickle
│ │ ├── dataset
│ │ ├── dataset.pickle
│ │ ├── marklinesEdges.p
│ ├── 03_models # Saved GNN models
│ └── 04_results # Results from the analysis 🚀
├── images
│ └── kg_extract.png
├── main.py
├── notebooks # 👨🏻💻 Exploratory notebooks
│ ├── 1_analyse_dgl_creation.ipynb
│ ├── 2_parameter_sweep-Copy1.ipynb
├── requirements.txt
└── src
├── __init__.py
├── common
│ └── formats.py
├── exploration # 🎣 Exploring data (e.g. degree distributions)
│ ├── Marklines.py
│ ├── __init__.py
│ ├── dataset.py
│ ├── visualise_graph.py
│ └── visualise_knowledge_graph.py
├── ingestion # Data loaders and utils for torch 🔥
│ ├── __init__.py
│ ├── dataloader.py
│ ├── dataset.py
│ ├── dgl_dataset.py
│ └── utils.py
├── managers # Training and testing managers in torch 🔥
│ ├── evaluator.py
│ └── trainer.py
├── model # 🤖 DGL Models
│ ├── __init__.py
│ └── dgl
│ ├── StochasticRGCN.py
│ ├── __init__.py
│ ├── __pycache__
│ │ ├── StochasticRGCN.cpython-39.pyc
│ │ ├── __init__.cpython-39.pyc
│ │ └── layers.cpython-39.pyc
│ └── layers.py