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..
Exponential Family Graph Embeddings
Authors: Abdulkadir Celikkanat, Fragkiskos D. Malliaros3357-3364
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental evaluation on real-world datasets demonstrates that the proposed techniques outperform well-known baseline methods in two downstream machine learning tasks. |
| Researcher Affiliation | Academia | Abdulkadir C elikkanat Centrale Sup elec and Inria Saclay University of Paris-Saclay Gif-Sur-Yvette, France EMAIL Fragkiskos D. Malliaros Centrale Sup elec and Inria Saclay University of Paris-Saclay Gif-Sur-Yvette, France EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks that are clearly labeled as such or formatted like code procedures. |
| Open Source Code | Yes | Source code. The implementation of the proposed models is provided in the following website: https://abdcelikkanat.github.io/projects/EFGE/. |
| Open Datasets | Yes | Table 1: Statistics of network datasets used in the experiments. |V|: number of nodes, |E|: number of edges, |K|: number of labels and |C|: number of connected components. |
| Dataset Splits | Yes | In our experiments, we split the nodes into varying training ratios, from 2% up to 90% in order to better evaluate the models. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact CPU/GPU models, memory, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'scikit-learn package' but does not specify its version number or any other software dependencies with version details, which are necessary for reproducibility. |
| Experiment Setup | Yes | For the optimization we use Stochastic Gradient Descent (SGD) (Bottou 1991) to learn representations Ω = (α, β)... we adopt the negative sampling strategy, setting sampling size to k = 5 in all the experiments. ... we have used walk length L = 10, number of walks N = 80 and window size γ = 10 for all models and the variants of EFGE model are fed with the same node sequences produced by NODE2VEC. |