GraphProt: Certified Black-Box Shielding Against Backdoored Graph Models
Authors: Xiao Yang, Yuni Lai, Kai Zhou, Gaolei Li, Jianhua Li, Hang Zhang
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present the results of our comparative analyses and ablation studies on GRAPHPROT. Notably, it functions within rigorous black-box conditions (with access limited to the test graph and few queries). Consequently, we primarily assess whether our approach achieves comparable efficacy to current defense strategies. ... Empirical evaluations conducted on multiple attack paradigms and benchmark datasets confirm its effectiveness in reducing attack success rates while preserving benign data accuracy. |
| Researcher Affiliation | Academia | 1Shanghai Jiao Tong University 2Hong Kong Polytechnic University 3Cornell University {youngshall, gaolei li, lijh888}sjtu.edu.cn, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology in text and uses a figure (Figure 1) to depict the workflow, but no structured pseudocode or algorithm blocks are provided. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide links to a code repository. |
| Open Datasets | Yes | We employed 6 benchmark datasets: AIDS [Rossi and Ahmed, 2015], ENZYMES [Dobson and Doig, 2003], DHFR [Morris et al., 2020], NCI1 [Wale and Karypis, 2006], PROTEINS [Borgwardt et al., 2005], and COLLAB [Yanardag and Vishwanathan, 2015]. |
| Dataset Splits | Yes | For each dataset, we randomly allocated two-thirds of the graphs for training the backdoored victim model, preserving the remainder for empirical testing. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., GPU models, CPU types). |
| Software Dependencies | No | The paper mentions GNN models like GCN, SAGE, and GAT, but does not specify any software libraries or frameworks with their version numbers. |
| Experiment Setup | Yes | The GRAPHPROT configuration includes the subgraph number K= 5, the samplerate p = 0.2, and the feature selection proportion r = 0.8. ... Multiple subgraph numbers K were configured at intervals of 3, ranging from 1 to 22... Sample-rates (0 – 100%) were applied for GRAPHPROT-R... We subsequently adjusted the feature fraction r of GRAPHPROT-TF across [0, 100]%... |