Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Hypergraph Attacks via Injecting Homogeneous Nodes into Elite Hyperedges
Authors: Meixia He, Peican Zhu, Keke Tang, Yangming Guo
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on five authentic datasets to validate the effectiveness of IE-Attack and the corresponding superiority to state-of-the-art methods. |
| Researcher Affiliation | Academia | 1School of Artificial Intelligence, Optics and Electronics (i OPEN), Northwestern Polytechnical University 2Cyberspace Institute of Advanced Technology, Guangzhou University 3Huangpu Research School, Guangzhou University 4School of Cybersecurity, Northwestern Polytechnical University |
| Pseudocode | No | The paper describes the methodology using mathematical equations and textual explanations, but it does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code for the described methodology, nor does it include any links to code repositories. |
| Open Datasets | Yes | To validate the superiority of our method, five datasets (Cora, Citeseer, Pubmed, Chameleon, Lastfm) (Maurya, Liu, and Murata 2021) are adopted. |
| Dataset Splits | Yes | According to the datasets partitioning strategy for node classification in Graph Convolutional Networks (GCNs) (Kipf and Welling 2017), datasets are divided into training/validation/test sets. |
| Hardware Specification | Yes | All experiments are conducted on a workstation equipped with four NVIDIA RTX 3090 GPUs, which are conducted under the same parameter settings. |
| Software Dependencies | No | The paper mentions software like HGNNs and GCNs, but does not provide specific version numbers for any libraries, frameworks, or programming languages used. |
| Experiment Setup | Yes | Parameter Setting In this study, we set the elite hyperedge perturbation budget η, which involves selecting η ω hyperedges within elite hyperedge Eelite. The value of η ranges from 0.1 to 1 and ω is the number of Eelite. Regarding the value of K in the Hyper-KNN hypergraph construction method, we set it to 10. The order in Hyper-HOR is set to 1-order and γ in Hyper-L1 is set to 0.1. ... Except for Table 1, the random seed for other experiments is set 2024. |