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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
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. |