Notice: 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. Full accuracy metrics and methodology are described in [1].
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
| 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. |