cave-g-f/gcn_partitioning

Graph Partitoning Using Graph Convolutional Networks

GCN Partitioning

Graph Partitoning Using Graph Convolutional Networks as described in GAP: Generalizable Approximate Graph Partitioning Framework

Loss Backward Equations

To handle large graphs, the loss function is implemented using sparse torch tensors using a custom loss class.

If

where Y_{ij} is the probability of node i being in partition j.

Then the gradients can be calculated by the equations:

Installation

Create a virtual environment using venv

python3 -m venv env

Source the virtual environment

source env/bin/activate

Use the package manager pip to install requirements.

pip install -r requirements.txt

Usage

python TrialModel.py

Limitations

Has only been tested on small custom graphs.

License

MIT