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