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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Recurrent Dirichlet Belief Networks for interpretable Dynamic Relational Data Modelling
Authors: Yaqiong Li, Xuhui Fan, Ling Chen, Bin Li, Zheng Yu, Scott A. Sisson
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The extensive experiment results on real-world data validate the advantages of the Recurrent-DBN over the state-of-the-art models in interpretable latent structure discovery and improved link prediction performance. |
| Researcher Affiliation | Academia | 1Centre for Artificial Intelligence, University of Technology Sydney 2School of Mathematics & Statistics, University of New South Wales, Sydney 3School of Computer Science, Fudan University 4Department of Electrical and Computer Engineering, University of Alberta |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found. The paper describes the generative process and inference steps textually and mathematically but not in a pseudocode format. |
| Open Source Code | No | No concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) was found for the methodology described in this paper. |
| Open Datasets | Yes | The real-world relational data sets used in this paper are: Coleman [Coleman, 1964], Mining Reality [Eagle and Pentland, 2006], Hypertext [Isella et al., 2011], Infectious [Isella et al., 2011] and Student Net [Fan et al., 2014]. |
| Dataset Splits | No | The paper specifies a 90% training and 10% test split. It does not mention a separate validation split or explicit details for validation data partitioning. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running the experiments were provided. |
| Software Dependencies | No | No specific ancillary software details, such as library or solver names with version numbers, were provided. |
| Experiment Setup | Yes | For the hyperparameters, we specify M Gamma(N, 1) for all data sets, {c(l) c , c(l) u }l, d, dc and Λk1,k2 are all given Gamma(1, 1) priors and L = 3. For MMSB, we set the membership distribution according to Dirichlet(11 K). Each run uses 3000 MCMC iterations with the first 1500 discarded as burn-in. |