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..
NetGAN without GAN: From Random Walks to Low-Rank Approximations
Authors: Luca Rendsburg, Holger Heidrich, Ulrike Von Luxburg
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6. Experiments The purpose of this section is to (i) verify that CELL has performance comparable to Net GAN while being much faster, and (ii) demonstrate the importance of the cross-entropy loss and benefit of the logit-transformation by comparing with other low-rank approximation baselines. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Tübingen, Germany 2Max Planck Institute for Intelligent Systems, Tübingen, Germany. |
| Pseudocode | Yes | Algorithm 1 Cross-Entropy Low-rank Logits (CELL) |
| Open Source Code | Yes | 1Code available at https://github.com/hheidrich/CELL |
| Open Datasets | Yes | Data sets and preprocessing. We experiment on a variety of graph data sets: the citation networks CORA-ML (Mc Callum et al., 2000) and CITESEER (Sen et al., 2008), the political blogs network POLBLOGS (Adamic & Glance, 2005), the retweet network RT-GOP, and the web graph WEB-EDU (Gleich et al., 2004). |
| Dataset Splits | Yes | For evaluating the link prediction performance during and after training, we split each graph into training-, validation-, and test-set by taking out 10% of the edges for validaton and another 5% for testing, while ensuring that the remaining graph stays connected. |
| Hardware Specification | No | Net GAN requires a GPU for training, while CELL runs on a CPU. |
| Software Dependencies | No | For solving optimization problem (10), we factorize W Wdown Wup with Wdown P RNˆH and Wup P RHˆN to satisfy the rank constraint, and optimize with Adam (Kingma & Ba, 2014). |
| Experiment Setup | Yes | To make the results comparable, we train CELL and Net GAN until the same stopping criterion of 52% edge overlap with the input graph is satisfied... using the logit space still has the advantage of requiring only a small rank (H 9 for CELL as compared to H 950 for LR-CE), which results in less trainable parameters and shorter training time. |