HyperDefender: A Robust Framework for Hyperbolic GNNs
Authors: Nikita Malik, Rahul Gupta, Sandeep Kumar
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that Hyper Defender significantly improves node classification accuracy across various attacks, effectively mitigating the performance degradation typically observed in Hy-GNNs when the hierarchy in original datasets is compromised. ... Section 4. Experiments |
| Researcher Affiliation | Academia | 1Bharti School of Telecommunication Technology and Management, Indian Institute of Technology, Delhi, India 2Department of Mathematics and Computing, Indian Institute of Technology, Delhi, India 3Department of Electrical Engineering, Indian Institute of Technology, Delhi, India 4Yardi School of Artificial Intelligence, Indian Institute of Technology, Delhi, India EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Hyper Defender Framework ... Algorithm 2: Pro-Hy La |
| Open Source Code | Yes | Code https://github.com/nikimal99/Hyper Defender.git |
| Open Datasets | Yes | We evaluate our proposed approach on three diverse datasets: Cora (Sen et al. 2008), Disease (Anderson and May 1991) and Airport (Zhang and Chen 2018). Dataset statistics are summarized in Table 1. |
| Dataset Splits | No | The paper does not explicitly provide specific training/validation/test dataset splits (e.g., percentages, sample counts, or citations to predefined splits) needed to reproduce the experiment. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, PyTorch 1.9) needed to replicate the experiment. |
| Experiment Setup | No | Algorithm 2 lists "Hyper-parameters α, β, γ, λ, τ, Learning rate η, η , Hy La feature dimension d1" as inputs, but the paper does not provide concrete values for these hyperparameters or other system-level training settings in the main text. |