ASSESS

Estimating Node Abnormalities from Imprecise Subgraph-Level Supervision (Z. Peng, Y. Xue, Y. Wang, Q. Lin and C. Shen, TNSE 2025): https://ieeexplore.ieee.org/document/11098617

Overview

The repository is organized as follows:

• data/ includes an example dataset and corresponding RWR random walk results;
• models/ contains the implementation of the ASSESS pipeline (weakad.py);
• layers/ contains the implementation of a standard GCN layer (gcn.py), the bilinear discriminator (discriminator.py), and the mean-pooling operator (avgneighbor.py);
• utils/ contains the necessary processing tool (process.py).

You could further optimize the code based on your own needs. We display it in an easy-to-read form.

Requirements

• PyTorch 2.3.1
• Python 3.11
• NetworkX 3.3
• graph-walker (https://github.com/kerighan/graph-walker)

Usage

python execute.py

Cite

Please cite our paper if you make use of ASSESS in your research:

@article{11098617,
title={Estimating Node Abnormalities From Imprecise Subgraph-Level Supervision},
author={Peng, Zhen and Xue, Yunqi and Wang, Yunfan and Lin, Qika and Shen, Chao},
journal={IEEE Transactions on Network Science and Engineering},
year={2025},
doi={10.1109/TNSE.2025.3593338}
}