TOPOAUC

An implement of the ACM MM 22 paper: A Unified Framework against Topology and Class Imbalance.

Environments

• Python 3.9.15
• Mindspore 2.0.0 nightly
• CUDA 11.6
• Ubuntu 18.04

Data:

Before the whole process, the dataset cora/citeseer/pubmed would be downloaded to data\dataset_name\ and Convert their data types as mindrecord.

You would download the datast in: here

Then, through the scripts provided by mindspore, they can be converted to mindrecord format.

For example as Cora:

!git clone https://gitee.com/mindspore/models.git
SRC_PATH = "./cora"
MINDRECORD_PATH = "./cora_mindrecord"

!rm -rf $MINDRECORD_PATH
!mkdir $MINDRECORD_PATH

!python models/utils/graph_to_mindrecord/writer.py --mindrecord_script cora --mindrecord_file "$MINDRECORD_PATH/cora_mr" --mindrecord_partitions 1 --mindrecord_header_size_by_bit 18 --mindrecord_page_size_by_bit 20 --graph_api_args "$SRC_PATH"

in the end, The dataset should be organized into the following form：

ms_GraphAUC-main
├─data
│  ├─cora
│  │  ├─cora_mr
│  │  └─cora_mr.db
│  │ 
│  ├─citeseer
│  │  ├─citeseer_mr
│  │  └─citeseer_mr.db
│  │ 
│  └─pubmed
│     ├─pubmed_mr
│     └─pubmed_mr.db
...

Train:

When runing train.py for the default config, you would change the hyper-parameter by change config.py

Eval:

You can run python eval.py to test the final performance of the selected CKPT file.