Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach
Authors: Kai Zhao, Qiyu Kang, Yang Song, Rui She, Sijie Wang, Wee Peng Tay
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 38,734 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,221 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The adversarial robustness of different neural flow GNNs is empirically compared on several benchmark datasets under a variety of adversarial attacks. Extensive numerical experiments demonstrate that GNNs leveraging conservative Hamiltonian flows with Lyapunov stability substantially improve robustness against adversarial perturbations. |
| Researcher Affiliation | Collaboration | Kai Zhao Nanyang Technological University Qiyu Kang Nanyang Technological University Yang Song C3 AI, Singapore Rui She Nanyang Technological University Sijie Wang Nanyang Technological University Wee Peng Tay Nanyang Technological University |
| Pseudocode | Yes | Algorithm 1: Graph Node Embedding Learning with HANG |
| Open Source Code | Yes | The implementation code of experiments is available at https://github.com/zknus/Neur IPS-2023-HANG-Robustness. |
| Open Datasets | Yes | Our datasets include citation networks (Cora, Citeseer, Pubmed) [51], the Coauthor academic network [52], an Amazon co-purchase network (Computers) [52], and the Ogbn-Arxiv dataset [53]. |
| Dataset Splits | Yes | For inductive learning, we follow the data splitting method in the GRB framework [54], with 60% for training, 10% for validation, and 20% for testing. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts) used for running experiments were mentioned. Only general statements like 'on a cluster' were found. |
| Software Dependencies | No | The ODE is solved using the solver from [18]. We consider fixed-step Euler and RK4, along with adaptive-step Dopri5, from [18], and Symplectic-Euler from [35]. While specific solvers are named, overall software dependencies (e.g., Python, PyTorch versions) are not provided with specific version numbers. |
| Experiment Setup | Yes | The raw node features are compressed to a fixed dimension, such as 64, using a fully connected (FC) layer to generate the initial features q(0)...For all datasets, we establish the time T as 3 and maintain a fixed step size of 1. This setup aligns with a fair comparison to three-layer GNNs. |