Graph Structure Learning with Variational Information Bottleneck
Authors: Qingyun Sun, Jianxin Li, Hao Peng, Jia Wu, Xingcheng Fu, Cheng Ji, Philip S Yu4165-4174
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results demonstrate that the superior effectiveness and robustness of VIB-GSL. |
| Researcher Affiliation | Academia | 1 Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing 100191, China 2 School of Computer Science and Engineering, Beihang University, Beijing 100191, China 3 Shenyuan Honors College, Beihang University, Beijing 100191, China 4 School of Computing, Macquarie University, Sydney, Australia 5 Department of Computer Science, University of Illinois at Chicago, Chicago, USA |
| Pseudocode | Yes | Algorithm 1: The overall process of VIB-GSL |
| Open Source Code | Yes | 1Code is available at https://github.com/VIB-GSL/VIB-GSL. |
| Open Datasets | Yes | We empirically perform experiments on VIBGSL on four widely-used social datasets including IMDBB, IMDB-M, REDDIT-B, and COLLAB (Rossi and Ahmed 2015). |
| Dataset Splits | Yes | We perform 10-fold cross-validation and report the average accuracy and the standard deviation across the 10 folds in Table 1 |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for experiments. |
| Software Dependencies | No | The paper does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | Parameter Settings. We set both the information bottleneck size K and the embedding dimension of baseline methods as 16. For VIB-GSL, we set t = 0.1 in Eq. (13), a0 = 0.1 and perform hyperparameter search of β {10 1, 10 2, 10 3, 10 4, 10 5, 10 6} for each dataset. |