Skip to content

Latest commit

 

History

History
85 lines (54 loc) · 2.98 KB

README.md

File metadata and controls

85 lines (54 loc) · 2.98 KB
Raw

edGNN: A simple and powerful GNN for labeled graphs

The ability of a graph neural network (GNN) to leverage both the graph topology and graph labels is fundamental to build discriminative node and graph embeddings. Building on previous work, we theoretically show that our model for directed labeled graphs, edGNN, is as powerful as the Weisfeiler-Lehman algorithm for graph isomorphism. Our experiments support our theoretical findings, confirming that graph neural networks can be effectively used for inference problems on directed graphs with both node and edge labels.

Paper available [here].

Installation

Create a virtualenv:

python3 -m venv venv

Activate it:

source venv/bin/activate

Install the package as editable and any dependencies:

pip3 install -e .

Add the repository path to your PYTHONPATH:

export PYTHONPATH="<PATH>/edGNN:$PYTHONPATH"

Train a model

a. Preprocess:

preprocess <dataset_family> --dataset <datase_name> --out_folder <path_to_preprocessed_data>

Currently, the 2 dataset families are dortmund (includes PTC_FR, PTC_FM and PTC_MM, PTC_MR and MUTAG) for graph classification and dglrgcn (includes mutag and aifb) for node classification.

b. Run a model:

run_model --dataset <dataset_name> --config_fpath <path_to_configuration_files> --data_path <path_to_preprocessed_data>

Configuration files for our model edGNN and R-GCN can be found in core/models/config_files/ For other options to the run_model app, refer to the implementation.

Examples

a. Graph classification of the PTC_FR dataset:

(i) with edGNN:

cd bin && mkdir ../preprocessed_graphs && cd ../preprocessed_graphs && mkdir ptc_fr && cd ../bin/
preprocess dortmund --dataset ptc_fr --out_folder ../preprocessed_graphs/ptc_fr/
run_model --dataset ptc_fr  --config_fpath ../core/models/config_files/config_edGNN_graph_class_ptc.json  --data_path ../preprocessed_graphs/ptc_fr/ --n-epochs 40

Default config are the same as reported in the paper. PTC_FR accuracy around 65%.

(ii) with R-GCN:

run_model --dataset ptc_fr  --config_fpath ../core/models/config_files/config_RGCN_graph_class.json  --data_path ../preprocessed_graphs/ptc_fr/ --n-epochs 40

Default config are the same as reported in the paper. PTC_FR accuracy around 65%.

b. Node classification of the AIFB dataset:

cd bin && mkdir ../preprocessed_graphs && cd ../preprocessed_graphs && mkdir aifb && cd ../bin/
preprocess dglrgcn --dataset aifb --out_folder ../preprocessed_graphs/aifb --reverse_edges True
run_model --dataset aifb  --config_fpath ../core/models/config_files/config_edGNN_node_class.json  --data_path ../preprocessed_graphs/aifb/ --n-epochs 400 --weight-decay 0 --lr 0.005

Default config are the same as reported in the paper. AIFB accuracy around 91%.

Note that if you are running the code with a GPU, you should add the argument --gpu 0 to run_model.