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..
Convergence Guarantees for the DeepWalk Embedding on Block Models
Authors: Christopher Harker, Aditya Bhaskara
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On the experimental side, we validate our results: we show a clear separation between the embeddings of vertices across clusters for different choices of the embedding dimension. |
| Researcher Affiliation | Academia | 1Kahlert School of Computing, University of Utah, Salt Lake City, UT, USA. |
| Pseudocode | Yes | Algorithm 1 Deep Walk Gradient Descent |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper uses graphs drawn from a "stochastic block model (SBM)" which is a generative model for graphs. It does not provide access information (link, DOI, specific citation) for a publicly available, pre-existing dataset used for training or evaluation. |
| Dataset Splits | No | The paper describes generating graphs from a stochastic block model for experiments. It does not mention explicit training, validation, or test splits of a fixed dataset, as the data is generated rather than partitioned from an existing source. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware (e.g., CPU, GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software libraries, frameworks, or environments used in the experiments. |
| Experiment Setup | Yes | We run the algorithm for T = 100 iterations and used a learning rate of η = 0.01. The embeddings are initialized randomly so that x(t) 0.01 and y(t) 0.01. ... The learning rate was set for η = 1 n and training was rate for T = 75 iterations. |