Robust Graph Dictionary Learning
Authors: Weijie Liu, Jiahao Xie, Chao Zhang, Makoto Yamada, Nenggan Zheng, Hui Qian
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that our algorithm achieves good performance on both simulated and real-world datasets. This section provides empirical evidence that RGDL performs well in the unsupervised graph clustering task on both synthetic and real-world datasets |
| Researcher Affiliation | Collaboration | Weijie Liu,1,2 Jiahao Xie,2 Chao Zhang,2,8,9 Makoto Yamada,3,4,5 Nenggan Zheng,1,2,6,8 Hui Qian2,7,8 1 Qiushi Academy for Advanced Studies, Zhejiang University 2 College of Computer Science and Technology, Zhejiang University 3 Okinawa Institute of Science and Technology 4 Kyoto University 5 RIKEN AIP 6 Zhejiang Lab 7 State Key Lab of CAD&CG, Zhejiang University 8 Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies 9 Advanced Technology Institute, Zhejiang University |
| Pseudocode | Yes | Algorithm 1 Projected Gradient Descent for RGWD. Algorithm 2 Robust Graph Dictionary Learning (RGDL) |
| Open Source Code | Yes | Code available at https://github.com/cxxszz/rgdl. |
| Open Datasets | Yes | We first test RGDL in the graph clustering task on datasets simulated according to the well-studied Stochastic Block Model (SBM) (Holland et al., 1983; Wang and Wong, 1987). We consider widely utilized benchmark datasets including MUTAG (Debnath et al., 1991), BZR (Sutherland et al., 2003), and Peking 1 (Pan et al., 2016). We further use RGDL to cluster real-world graphs. RGDL is thus compared against GDL (Vincent-Cuaz et al., 2021), GWF (Xu, 2020), and other state-of-the-art graph classification methods including WGDL (Zhang et al., 2021) and GNTK (Du et al., 2019) on the benchmark datasets MUTAG (Debnath et al., 1991), IMDB-B, and IMDB-M (Yanardag and Vishwanathan, 2015). |
| Dataset Splits | Yes | We perform a 10-fold nested cross validation (using 9 folds for training, 1 for testing, and reporting the average accuracy of this experiment repeated 10 times) by keeping same folds across methods. |
| Hardware Specification | No | The paper does not specify any particular hardware components (e.g., GPU models, CPU types, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like 'kmeans algorithm' and '3-NN as the classifier' but does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | The heat kernel matrix is employed for the PRM. The number of atoms M is set as M = β(# classes) where β is chosen from {2, 3, 4, 5}. RGDL is run with different values of ϵ. Specifically, ϵ is chosen from {U, 10−1U, 10−2U, 10−3U} where U = maxk∈JKK Ck. We further conduct sensitivity analysis of λ by varying the value in {0, 10−5, 10−4, 10−3, 10−2, 10−1}. RGDL, GDL, and GWF use 3-NN as the classifier due to its simplicity. |