Effective Clustering for Large Multi-Relational Graphs

Environment Settings

• torch ---- 2.0.1+cu118

• torch-cluster --- 1.6.1+pt20cu118

• torch-geometric ---- 2.6.1

• torch-scatter ---- 2.1.1+pt20cu118

• torch-sparse ---- 0.6.17+pt20cu118

• torch-spline-conv ---- 1.2.2+pt20cu118

• pyg-lib ---- 0.2.0+pt20cu118

• scipy ---- 1.13.1

• scikit-learn ---- 1.5.2

• numpy ---- 1.26.3

How to run

For DEMM+, you can run the dataset "ACM" by following example:

python main.py  --L 5 --alpha 4 --dataset acm-3025 --gamma 0. 
--dim 128  --seed 6  --beta 2.5 --method demm+ --m 10 14  --gpu 0

 For DEMM, you can run the dataset "ACM" by following example:

python demm-main.py  --alpha 2 --dataset acm-3025 --gamma 0. --seed 6 
--beta 2 --gpu 0

For DEMM-AL, you can run the dataset "ACM" by following example:

python main.py   --dataset acm-3025 --gamma 0. --dim 6  --seed 6 
 --beta 2 --method demmal --m 10 10 --gpu 0
 

For more details about following datasets , you can refer to 'run.sh'.

Dataset

 Due to space constraints, we have placed all datasets except "oag-cs", "oag-eng", and "rcdd" in the ​​data.zip​​ file, which can be accessed via the following link: Link to data.zip.

For the "oag-cs", "oag-eng", and "rcdd" datasets, their original data access details are provided below:

oag-cs: Link to OAG-CS

oag-eng: Link to OAG-ENG

rcdd: Link to RCDD