- python (==3.8)
- torch (==1.10.0)
- pyg (==2.0.2)
- torch-scatter (==1.5.9)
- torch-sparse (==0.6.12)
- networkx (==2.6.2)
- GraphRicciCurvature (==0.5.3)
- igraph (==0.9.8)
- tabulate (==0.8.9)
- GraKeL (==0.1.8)
- numpy (==1.21.0)
- pandas (==1.2.5)
- sklearn (==0.24.2)
- scipy (==1.7.0)
Add dataset name in tmp_ds.txt
which is available in https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldataset
. And we have provided build_all_feg.sh
and build_all_fes.sh
in sh
to download all data and build filtration-enhanced graphs and snapshots.
To download dataset and build FEG with filtration of native edge weight(or native vertex attributes), navigate to sh
folder and type the following command into the terminal:
$ ./build_all_feg.sh attr
$ ./build_all_feg.sh vattr
To run Weisfeiler-Leman Subtree kernel, ShortestPath Kernel and GraphLet Kernel for datasets with filtration of native edge weight, navigate to sh
folder and type the following command into the terminal:
$ ./run_kernel.sh attr [directory name]
To run Weisfeiler-Leman Subtree kernel, ShortestPath Kernel and GraphLet Kernel for datasets with filtration of native vertex attributes, navigate to sh
folder and type the following command into the terminal:
$ ./run_kernel_snapshot.sh vattr [directory name]
To run GIN for datasets with filtration of core number, navigate to sh
folder and type the following command into the terminal:
$ ./run_gin.sh gin [lr] [dropout] degeneracy
To run GIN for datasets with filtration of ricci-curvature, navigate to sh
folder and type the following command into the terminal:
$ ./run_sage.sh [lr] [dropout] curvature
To run GraphSNN for datasets with filtration of ricci-curvature, navigate to sh
folder and type the following command into the terminal:
$ ./run_graphsnn.sh [lr] [dropout] curvature