Integrated Defense for Resilient Graph Matching
Authors: Jiaxiang Ren, Zijie Zhang, Jiayin Jin, Xin Zhao, Sixing Wu, Yang Zhou, Yelong Shen, Tianshi Che, Ruoming Jin, Dejing Dou
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the robustness of our IDRGM model on real datasets against state-of-the-art algorithms.Empirical evaluation over real graph datasets demonstrates that the remarkable robustness of IDRGM against state-of-the-art graph matching methods and representative resilient Lipschitz-bound neural architectures. We report Hits@K (Yasar & C ataly urek, 2018; Fey et al., 2020) to evaluate and compare our model to previous lines of work, where Hits@K measures the proportion of correctly matched nodes ranked in the top-K list. |
| Researcher Affiliation | Collaboration | 1Auburn University, USA 2Peking University, China 3Microsoft Dynamics 365 AI, USA 4Kent State University, USA 5University of Oregon, USA 6Baidu Research, China. |
| Pseudocode | Yes | Algorithm 1 Expressive Parameter Estimation |
| Open Source Code | No | The paper states: "To our best knowledge, there are no other open-source defense baselines on graph matching available." However, it does not explicitly provide its own source code or a link to it for the methodology described. |
| Open Datasets | Yes | We will show the robustness of our IDRGM model for resilient graph matching over three datasets: autonomous systems (AS) (AS), CAIDA relationships datasets (CAI), and DBLP coauthor graphs (DBL), as shown in Table 1. Links provided in references: https://snap.stanford.edu/data/Oregon-2.html. https://snap.stanford.edu/data/as-Caida.html. http://dblp.uni-trier.de/xml/. |
| Dataset Splits | No | The dataset is divided into two disjoint sets: training data D and test data D .We randomly sample 30% of known matched node pairs as training data and the rest as test data. The paper specifies training and test data splits but does not explicitly mention a separate validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | In addition, more experiments, implementation details, and hyperparameter selection and setting are presented in Appendices A.2-A.4. These details are not explicitly present in the main text of the paper as required. |