Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
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 | Venue PDF | LLM Run Details | Input Tokens: 18,357 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,396 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |