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..
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks
Authors: Pál András Papp, Karolis Martinkus, Lukas Faber, Roger Wattenhofer
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally validate our theoretical findings on expressiveness. Furthermore, we show that Drop GNNs perform competitively on established GNN benchmarks. |
| Researcher Affiliation | Academia | Pál András Papp ETH Zurich EMAIL Karolis Martinkus ETH Zurich EMAIL Lukas Faber ETH Zurich EMAIL Roger Wattenhofer ETH Zurich EMAIL |
| Pseudocode | No | The paper does not include pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is publicly available1. |
| Open Datasets | Yes | We use the datasets from Sato et al. [29] that are based on 3 regular graphs. Nodes have to predict whether they are part of a triangle (TRIANGLES) or have to predict their local clustering coefficient (LCC). We test on the two counterexamples LIMITS 1 (Figure 2a) and LIMITS 2 from Garg et al. [11] |
| Dataset Splits | Yes | Following previous work [22; 21] the data is split into 80% training, 10% validation, and 10% test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions software but does not specify version numbers for key components (e.g., Python, PyTorch) that would be needed for replication. |
| Experiment Setup | Yes | Unless stated otherwise, we set the number of runs to m and choose the dropout probability to be p = 1/m, where m is the mean number of nodes in the graphs in the dataset. |