Generative Graph Dictionary Learning
Authors: Zhichen Zeng, Ruike Zhu, Yinglong Xia, Hanqing Zeng, Hanghang Tong
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the effectiveness of the obtained node and graph embeddings, and our algorithm achieves significant improvements over the state-of-the-art methods. and Extensive experiments show that FRAME achieves significant improvement on graph-level and node-level tasks, outperforming the state-of-the-art by 8.0% on graph classification, 0.5% on graph clustering, and 2.5% on node clustering, respectively. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA 2Meta, CA, USA. |
| Pseudocode | Yes | Algorithm 1 FRAME |
| Open Source Code | Yes | The code is implemented by authors from the University of Illinois and available at https://github.com/zhichenz98/FraMe-ICML23. |
| Open Datasets | Yes | All the real-world datasets we use are from (Morris et al., 2020) and available online1. 1https://chrsmrrs.github.io/datasets/ and lists datasets like "ENZYMES (Borgwardt et al., 2005)". |
| Dataset Splits | Yes | For graph classification, we apply 10-fold cross-validation on the benchmark datasets. |
| Hardware Specification | Yes | All experiments are conducted on the Linux platform with an Intel Xeon Gold 6240R CPU and an NVIDIA Tesla V100 SXM2 GPU. |
| Software Dependencies | No | The paper mentions specific software libraries used (POT toolbox, Gra Kel library, Karate Club library) but does not provide their version numbers. |
| Experiment Setup | No | The paper refers to hyperparameters (α, q, T, L) in Algorithm 1 and discusses the effect of σ in Section 4.4, but it does not provide specific numerical values for these or other training configurations (e.g., learning rate, batch size, optimizer settings) in the main text. |