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..
Manifold structure in graph embeddings
Authors: Patrick Rubin-Delanchy
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Figure 1 shows point clouds obtained by adjacency spectral embedding graphs from three latent position network models on R (with n = 5000, ˆD = 3). For experimental parameters a = 5, b = 2, normal(0, 1), the kernel f(x, y) = 1 exp( 2xy) (seen earlier), ˆD = 100 and n = 5000, split into 3,000 training and 2,000 test examples, the out-ofsample mean square error (MSE) of four methods are compared: a feedforward neural network (using default R keras configuration with obvious adjustments for input dimension and loss function; MSE 1.25); the random forest [10] (default configuration of the R package random Forest; MSE 1.11); the Lasso [67] (default R glmnet configuration; MSE 1.58); and least-squares (MSE 1.63). |
| Researcher Affiliation | Academia | Patrick Rubin-Delanchy University of Bristol EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the methodology described. It mentions using existing R packages (e.g., 'R package igraph', 'R keras configuration', 'R package random Forest', 'R glmnet configuration', 'R package ks', 'R package intrinsicDimension') but not its own implementation code. |
| Open Datasets | Yes | Figure 2: Non-linear dimension reduction of spectral embeddings. a) Graph of computer-to-computer network flow events on the Los Alamos National Laboratory network, from the publically available dataset [36] ... c) graph of consumer-restaurant ratings... extracted from the publically available Yelp dataset |
| Dataset Splits | No | The paper mentions 'split into 3,000 training and 2,000 test examples' but does not explicitly state a separate validation split or subset. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions several R packages (e.g., 'R keras', 'R package random Forest', 'R glmnet', 'R package igraph', 'R package ks', 'R package intrinsicDimension') but does not specify their version numbers. |
| Experiment Setup | Yes | For experimental parameters a = 5, b = 2, normal(0, 1), the kernel f(x, y) = 1 exp( 2xy) (seen earlier), ˆD = 100 and n = 5000, split into 3,000 training and 2,000 test examples, the out-ofsample mean square error (MSE) of four methods are compared: a feedforward neural network (using default R keras configuration with obvious adjustments for input dimension and loss function; MSE 1.25); the random forest [10] (default configuration of the R package random Forest; MSE 1.11); the Lasso [67] (default R glmnet configuration; MSE 1.58); and least-squares (MSE 1.63). |