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..
Overlapping Spaces for Compact Graph Representations
Authors: Kirill Shevkunov, Liudmila Prokhorenkova
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments confirm that overlapping spaces outperform the competitors in graph embedding tasks with different evaluation metrics. We also perform an empirical analysis in a realistic information retrieval setup, where we compare all spaces by incorporating them into DSSM. |
| Researcher Affiliation | Collaboration | Kirill Shevkunov Yandex, MIPT Moscow, Russia EMAIL Liudmila Prokhorenkova Yandex Research, MIPT, HSE University Moscow, Russia EMAIL |
| Pseudocode | No | The paper includes Figure 1 which illustrates the overlapping space computation, but it is a diagram, not structured pseudocode or an algorithm block. |
| Open Source Code | Yes | The code of our experiments is available.6 https://github.com/shevkunov/overlapping-spaces-for-compact-graph-representations |
| Open Datasets | Yes | We use the following graph datasets: the USCA312 dataset of distances between North American cities [4] (weighted complete graph), a graph of computer science Ph.D. advisoradvisee relationships [1], a power grid distribution network with backbone structure [28], a dense social network from Facebook [17], and Eu Core dataset generated using email data from a large European research institution [16]. We also collected a new dataset by launching the breadth-first search on the Wikipedia category graph, starting from the Linear Algebra category with search depth limited to 6. Further, we refer to this dataset as WLA6; more details are given in Appendix A.2. |
| Dataset Splits | Yes | All queries are divided into train, validation, and test sets, and for each signature, the optimal iteration was selected on the validation set. |
| Hardware Specification | No | The paper states: 'Technical requirements for our implementation are provided with code, small experiments can be reproduced on a regular computer.' This statement is too general and does not provide specific hardware details (e.g., CPU/GPU models, memory, specific cluster names) used for the experiments. |
| Software Dependencies | No | The paper states: 'Technical requirements for our implementation are provided with code'. However, the paper text itself does not list specific software dependencies with their version numbers. |
| Experiment Setup | Yes | The training details are given in Appendix A. |