On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation
Authors: Zhanke Zhou, Chenyu Zhou, Xuan Li, Jiangchao Yao, Quanming Yao, Bo Han
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we achieve state-of-the-art results on six datasets and three common GNNs. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, Hong Kong Baptist University 2Cooperative Medianet Innovation Center, Shanghai Jiao Tong University 3Shanghai AI Laboratory 4Department of Electronic Engineering, Tsinghua Unversity. |
| Pseudocode | Yes | Algorithm 1 Markov Chain-based Graph Reconstruction Attack. Algorithm 2 Markov Chain-based Defensive Training Against Graph Reconstruction Attack. |
| Open Source Code | Yes | The code is publicly available at: https: //github.com/tmlr-group/MC-GRA. |
| Open Datasets | Yes | Six common datasets are utilized in experiments, which are collected from four diverse domains: (1) Cora and Citeseer (Sen et al., 2008) are citation networks... (2) Polblogs (Adamic & Glance, 2005) is a social network... (3) USA and Brazil (Ribeiro et al., 2017) are air-traffic networks... (4) AIDS (Riesen et al., 2008) is a chemical network... |
| Dataset Splits | Yes | The dataset split setting of train/validate/test sets is consistent with other datasets used in this work. |
| Hardware Specification | Yes | The implementation software is Pytorch (Paszke et al., 2017) while the hardware is an NVIDIA RTX 3090 GPU. |
| Software Dependencies | No | The implementation software is Pytorch (Paszke et al., 2017)... It mentions PyTorch but does not specify its version number. |
| Experiment Setup | Yes | The optimal hyperparameters are obtained by random search or grid search (La Valle et al., 2004). |