Graph Random Neural Features for Distance-Preserving Graph Representations
Authors: Daniele Zambon, Cesare Alippi, Lorenzo Livi
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental campaign is divided into two parts. Section 8.1 gives empirical evidence about the claimed convergence as the embedding dimension M grows. Secondly, Section 8.2 shows that our method can be effectively used as a layer of a neural network and achieves results comparable to the current state of the art on classification tasks. |
| Researcher Affiliation | Academia | 1Universit a della Svizzera italiana, Lugano, Switzerland 2Politecnico di Milano, Milano, Italy 3University of Manitoba, Winnipeg, Canada 4University of Exeter, Exeter, United Kingdom. |
| Pseudocode | No | The paper does not contain a structured pseudocode or algorithm block, nor is there a section explicitly labeled 'Pseudocode' or 'Algorithm'. |
| 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 | Yes | Specifically, we considered NCI1, PROTEINS, ENZYMES, IMDB-BINARY, IMDB-MULTI and COLLAB, all available to the public (Kersting et al., 2016) and commonly used for benchmarking. |
| Dataset Splits | Yes | We report accuracy and standard deviation estimated on 10-fold cross-validation, where in each run we consider the optimal hyper-parameter configuration assessed on a validation set. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions PyTorch in the bibliography as a citation but does not specify its version number or any other software library names with their corresponding version numbers required for reproducibility. |
| Experiment Setup | Yes | All intermediate layers have the rectified linear unit function x 7 max{0, x} as activation function. We build features with k = 1, 2 tensor orders and embedding dimension M = 512. |