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..
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
Authors: Aleksandar Bojchevski, Stephan Günnemann
ICLR 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real world networks demonstrate the high performance of our approach, outperforming state-of-the-art network embedding methods on several different tasks. |
| Researcher Affiliation | Academia | Aleksandar Bojchevski, Stephan G unnemann Technical University of Munich, Germany EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. It describes the method using mathematical formulations and textual descriptions. |
| Open Source Code | Yes | We provide all datasets, the source code of G2G, and further supplementary material (https://www.kdd.in.tum.de/g2g). |
| Open Datasets | Yes | We use several attributed graph datasets. Cora (Mc Callum et al., 2000)... We provide all datasets, the source code of G2G, and further supplementary material (https://www.kdd.in.tum.de/g2g). |
| Dataset Splits | Yes | To evaluate the performance we hide a set of edges/non-edges from the original graph and train on the resulting graph. Similarly to Kipf & Welling (2016b) and Wang et al. (2016) we create a validation/test set that contains 5%/10% randomly selected edges respectively and equal number of randomly selected non-edges.We used the validation set for hyper-parameter tuning and early stopping and the test set only to report the performance. |
| Hardware Specification | Yes | In fact, for graphs beyond 15K nodes we had to revert to slow training on the CPU since the data did not fit on the GPU memory (12GB). |
| Software Dependencies | No | The paper mentions using Adam for optimization and rectifier units/exponential linear units as activation functions. However, it does not specify software versions for programming languages, libraries, or frameworks (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | The parameters are optimized using Adam (Kingma & Ba, 2014) with a fixed learning rate of 0.001. ... As a sensible default we recommend an encoder with a single hidden layer of size s1 = 512. ... small number of epochs T needed for convergence (T 2000 for all shown experiments, see e.g. Fig. 3(b)). |