KG-TOSA: A Task-Oriented graph SAmpler for GNN

Fig.1: The TOSG’s generic graph pattern is based on two parameters: (i) the direction (outgoing and incoming) predicates, and (i) the number of hops.

Installation

Clone the KGTOSA repo
Create KGTOSA Conda environment (Python 3.8) and install pip requirements.
Activate the KGTOSA environment

conda activate KGTOSA

Extract TOSG triples:

Node Classification

python -u TOSG_Extraction/TOSG_Extraction_NC.py --sparql_endpoint http://206.12.98.118:8890/sparql --graph_uri http://dblp.org --target_rel_uri https://dblp.org/rdf/schema#publishedIn --TOSG d1h1 --batch_size 1000000 --out_file DBLP-15M_PV --threads_count 32

Link Prediction

python -u TOSG_Extraction/TOSG_Extraction_LP.py --target_rel_uri=isConnectedTo --data_path=<path> --dataset=YAGO3-10 --TOSG=d1h1 --file_sep=tab

Transform NC TOSG dataset into PYG dataset

python -u DatasetTransformer/TSV_TO_PYG_dataset.py --traget_node_type=Paper --target_rel=publishedIn --csv_path=<path> --dataset_name=DBLP-15M_PV_d1h1 --file_sep=tab --split_rel=publish_year

Download KGTOSA NC datasets

Download KGTOSA LP datasets

YAGO3-10_FG_d2h1

WikiKG2_FG_d2h1

DBLP2023-010305_FG_d2h1

Reproduce KGTOSA Results:

Node Classification

# run RGCN  
python rgcn-KGTOSA.py --Dataset <DatasetPath>
# run GraphSaint  
python graph_saint_KGTOSA.py --Dataset <DatasetPath>
# run ShaDowSaint  
python graph_saint_Shadow_KGTOSA.py --Dataset <DatasetPath>
# run SeHGNN  
python SeHGNN/ogbn/main.py --Dataset <DatasetPath>
# run IBS
python  IBS/run_ogbn_ppr.py --with config/<Config_path>

Link Prediction
extract the dataset folder under the data folder under each method path

# run RGCN  
python RGCN/main.py --Dataset <DatasetName> --TargetRel <target_rel>
# run MorsE  
python Morse/main.py --dataset <DatasetName> --TargetRel <target_rel
# run LHGNN  
python LHGNN/main.py --dataset <DatasetName> --TargetRel <target_rel