Manifold structure in graph embeddings
Authors: Patrick Rubin-Delanchy
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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 patrick.rubin-delanchy@bristol.ac.uk |
| 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). |