Overlapping Spaces for Compact Graph Representations
Authors: Kirill Shevkunov, Liudmila Prokhorenkova
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 shevkunov.ks@phystech.edu Liudmila Prokhorenkova Yandex Research, MIPT, HSE University Moscow, Russia ostroumova-la@yandex.ru |
| 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. |