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.
Deep Amortized Relational Model with Group-Wise Hierarchical Generative Process
Authors: Huafeng Liu, Tong Zhou, Jiaqi Wang7550-7557
AAAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 18,211 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,700 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 | A series of experiments have been conducted on both synthetic and real-world datasets. The experimental results demonstrated that Da RM can obtain high performance on both community detection and link prediction tasks. |
| Researcher Affiliation | Academia | 1Beijing Key Lab of Traf๏ฌc Data Analysis and Mining, Beijing Jiaotong University, Beijing, China 2 Department of Mathematics, The University of Hong Kong, Hong Kong SAR, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | Dataset Several widely known citation datasets are used, namely, NIPS12 [Globerson et al. 2007], Cora, Cite Seer and Pubmed [Rossi and Ahmed 2015]. |
| Dataset Splits | Yes | For link prediction task, we hold out 10% and 5% of the links as our test set and validation set, respectively, and use the validation set to ๏ฌne-tune the hyperparameters. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | The hyper-parameter ฯ scales the similarity from [ 1, 1] to [ 1/ฯ, 1/ฯ], which is set as ฯ = 0.1 to obtain a more skewed distribution. Note that ฯ0 should be set to a small value, e.g., around 0.1, since the learned representations are well normalized. We take the average of AUC scores by running model on 10 random split of dataset. |