This is a reference implementation for PhUSION, a proximity-based unified framework for computing structural and positional node embeddings, which leverages well-established methods for calculating node proximity scores. The full paper can be found here: https://arxiv.org/abs/2102.13582
To run proximity/structural embedding codes:
python src/main.py --param parameters/structural/PPMI.json
To run graph classification codes under graph_embed directory
python main.py --param ../../parameters/graph/PPMI.json
input parameters
- All the parameters are stored in a single .json file(examples are in the parameter/ directory), which contains 6 fields:
- "input": path of input file(.mat file)
- "prox_option": "FaBP", "heat_kernel" or "netmf"
- "prox_params": dict of parameters needed.
- transform indicates which nonlinear transform you would like to use, 0 is no nonlinear transform, 1 stands for log transform and 2 for binary threshold
- threshold indicates for bineary transform, which threshold you would like to use
- "prox_file": filename of the intermediate proximity matrix copy
- "embed_option": "proximity" or "struct"
- "embed_params": dict of parameters needed.
- For proximity: dim
- For struct: time_pnts
- "output": path for output file(.npy)
data directory
- Include three subdirectories:
- origin: store the graphs(.mat file)
- proximity: store the intermediate data(.mat file, proximity matrix)
- embeded: store the embeded matrix(.npy file)
src directory
- Now different methods are stored in different .py files(PPMI is stored in src/proxi_methods/PPMI.py)
- eval subdirectory contains predict.py and dist.py.
- Run dist.py to eval the proximity matrix
example:
python3 src/eval/dist.py --input data/proximity/heat_kernel_struct.mat - Run predict.py to evaluate performance
example:
python src/eval/predict.py --input data/origin/usa-airports.edgelist --embedding data/embeded/structural/PPMI.npy --seed 0
- Run dist.py to eval the proximity matrix