acharkq/gnn-logic

GNN-logic

Code for the paper Logical Expressiveness of Graph Neural Networks.

Install

Run pip install -r requirements.txt to install all dependencies.

Generate synthetic graphs

The graphs used in the paper are in the zip-file datasets.zip. Just unzip them to src/data/datasets. The script expects 3 folders inside src/data/datasets named p1, p2 and p3. These folders contains the datasets for the properties , and described in the apendix F on DATA FOR THE EXPERIMENT WITH CLASSIFIER IN EQUATION (6).

To generate new graphs use the script in src/utils/graphs.py. There is a small description of the arguments in generate_dataset.

Replicate synthetic results

Run the script in src/main.py. The results will be printed to console and logged in src/logging/results. A single file will collect the last epoch for each experiment for each dataset.

Example: p2-0-0-acrgnn-aggS-readM-combMLP-cl1-L2 means:

• p2: the property, in this case .
• acrgnn: the network being benchmarked, in this case ACR-GNN.
• aggS: the aggregation used, can be S=SUM, M=MAX or A=AVG.
• readM: the readout used, can be S=SUM, M=MAX or A=AVG.
• combMLP: the combine used, can be SIMPLE or MLP. If SIMPLE a ReLU function is used to apply the non-linearity. If MLP, a 2 layer MLP is used, with batch normalization and ReLU activation function in the hidden layer. No activation function is used over the output.
• cl1: the number of layers in the MLP used to weight each component (h, agg, readout), refered as A, B and C in the paper, V, A and R in the code. If 0, no weighting is done. If 1, a Linear unit is used. If N, with N>1, a N layer MLP is used, with batch normalization and ReLU activation function in the hidden layer.
• L2: the number of layers of the GNN. 2 in this case.

Replicate PPI results

Run the script in src/run_ppi.py. The results will be printed to console and logged in src/logging/ppi. A single file will collect the last epoch for each GNN combination. A file with no extension will be created with the mean of 10 runs for each configuration and the standard deviation.