This repository is the official implementation of Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks(https://arxiv.org/abs/2012.08740).
To install requirements:
run python
pip install tensorflow-gpu
pip install tensoflow_addons
pip install numpy
pip install spektral
To run non model main:
run python 3.6
pip install dgl-cu101
pip install dynamicgem
pip install keras==2.2.4
pip install torch
pip install --user scipy==1.4.1
pip install sklearn
These are seperate because dynamicgem has a lot of specific dependencies that make it incompatible with the original environment
To train the model(s) in the paper, run:
main.ipynb
To train models which don't need to be trained in the paper, run:
non model main.ipynb
To evaluate the model(s) in the paper, run:
main.ipynb
To evaluate models which don't need to be trained in the paper, run:
non model main.ipynb
Pre-trained Models are not available yet. Models take around 5 minutes to train.
Our model achieves the following performance on :
| Model name | Accuracy | AUC | F1 |
|---|---|---|---|
| Graphsage | 77.56% | 86.46% | 69.77% |
| GCN | 78.34% | 89.12% | 69.45% |
| GAT | 78.17% | 87.76% | 68.81% |
| Dynaernn | 45.69% | 51.57% | 52.34% |
| Spectral | 76.22% | 50.26% | 66.63% |
| GCNLSTM | 77.48% | 86.5% | 70.56% |
| RNNGCN | 77.83% | 88.28% | 69.26% |
| TRNNGCN | 77.84% | 87.39% | 69.51% |
| Model name | Accuracy | AUC | F1 |
|---|---|---|---|
| Graphsage | 66.5% | 80.49% | 58.9% |
| GCN | 68.5% | 87.67% | 56.31% |
| GAT | 68.74% | 86.97% | 56.67% |
| Dynaernn | 37.36% | 51.06% | 41.63% |
| Spectral | 67.3% | 54.16% | 50% |
| GCNLSTM | 67.68% | 84.57% | 57.66% |
| RNNGCN | 68.55% | 85.99% | 57.85% |
| TRNNGCN | 68.65% | 85.85% | 57.58% |
| Model name | Accuracy | AUC | F1 |
|---|---|---|---|
| Graphsage | 28.8% | 56.37% | 16.38% |
| GCN | 29.24% | 55.93% | 18.75% |
| GAT | 31.85% | 55.92% | 15.54% |
| Dynaernn | 29.16% | 52.44% | 28.98% |
| Spectral | 32.02% | 50.14% | 15.93% |
| GCNLSTM | 31.23% | 56.7% | 20.93% |
| RNNGCN | 31.85% | 55.92% | 15.54% |
| TRNNGCN | 30.96% | 56.18% | 17.57% |
| Model name | Accuracy | AUC | F1 |
|---|---|---|---|
| Graphsage | 64.93% | 91.29% | 91.29% |
| GCN | 21.12% | 67.62% | 12.56% |
| GAT | 39.81% | 82.6% | 33.18% |
| Dynaernn | 26.28% | 58.61% | 26.01% |
| Spectral | 36.36% | 64.18% | 36.68% |
| GCNLSTM | 41.52% | 85.1% | 40.1% |
| RNNGCN | 30.04% | 76% | 24.66% |
| TRNNGCN | 21.94% | 66.42% | 15.58% |
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