Official Implementation of 'Partial Subgraph InfoMax (PSI)' from 'Models and Benchmarks for Representation Learning of Partially Observed Subgraphs', 31st ACM International Conference on Information and Knowledge Management (CIKM 2022, Short Papers Track).
TBA
bash PSI/install.sh ${CUDA, optional, default is cu102.}
- If you have any trouble installing PyTorch Geometric, please install PyG's dependencies manually.
- Codes are tested with python
3.7.9
andnvidia/cuda:10.2-cudnn8-devel-ubuntu18.04
image. - PYG's FAQ might be helpful.
- The main train/test code is in
PSI/main.py
. - If you want to see hyperparameter settings, refer to
PSI/args.yaml
andPSI/arguments.py
.
python -u PSI/main.py \
--dataset-name FNTN \
--custom-key BIE2D2F64-ISI-X-GB-PGA \
--gpu-ids 0 \
--dataset-path /mnt/nas2/GNN-DATA/
There are three arguments for GPU settings (--num-gpus-total
, --num-gpus-to-use
, --gpu-ids
).
Default values are from the author's machine, so we recommend you modify these values from PSI/args.yaml
or by the command line.
--num-gpus-total
(default 4): The total number of GPUs in your machine.--num-gpus-to-use
(default 1): The number of GPUs you want to use.--gpu-ids
(default:[0]
): The ids of GPUs you want to use.
Dataset | --dataset-name |
---|---|
FNTN | FNTN |
EM-User | EMUser |
HPO-Metab | HPOMetab |
Download datasets and put them into the specific path (--dataset-path
).
root@5b592ce:~$ ls /mnt/nas2/GNN-DATA/
EMUSER FNTN HPOMETAB
Type | FNTN | EMUser & HPOMetab |
---|---|---|
PS-DGI | BISAGE-SHORT-DGI-X-GB-PGA | SAGE-SHORT-DGI-X-GB |
PS-InfoGraph | BISAGE-SHORT-ISI-X-GB-PGA | SAGE-SHORT-ISI-X-GB |
PS-MVGRL | BISAGE-SHORT-MVGRL-X-GB-PGA | SAGE-SHORT-MVGRL-X-GB |
PS-GraphCL | BISAGE-SHORT-GRAPHCL3-X-GB-PGA | SAGE-SHORT-GRAPHCL3FB-X-GB (only for HPOMetab) |
k-hop PSI | BIE2D2F64-X-PGA | E2D2F64-X |
k-hop PSI + PS-DGI | BIE2D2F64-DGI-X-GB-PGA | E2D2F64-DGI-X-GB |
k-hop PSI + PS-InfoGraph | BIE2D2F64-ISI-X-GB-PGA | E2D2F64-ISI-X-GB |
See PSI/args.yaml
or run $ python PSI/main.py --help
.