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..
Graph Neural Networks with Local Graph Parameters
Authors: Pablo Barceló, Floris Geerts, Juan Reutter, Maksimilian Ryschkov
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental evaluation shows that adding local graph parameters often has a positive effect on a variety of GNNs, datasets and graph learning tasks. |
| Researcher Affiliation | Academia | 1 Department of Computer Science, PUC, Chile 2 Millennium Institute for Foundational Research on Data, Chile 3 Department of Computer Science, University of Antwerp, Belgium [pbarcelo,jreutter]@ing.puc.cl, [floris.geerts,maksimilian.ryschkov]@uantwerpen.be |
| Pseudocode | No | The paper defines algorithms and processes using mathematical notation and descriptive text, but it does not include a clearly labeled pseudocode block or algorithm. |
| Open Source Code | Yes | Code to reproduce our experiments is available at https://github.com/Mr Ryschkov/LGP-GNN |
| Open Datasets | Yes | We select the best architectures from Dwivedi et al. [2020]: Graph Attention Networks (GAT) [Velickovic et al., 2018], Graph Convolutional Networks (GCN) [Kipf and Welling, 2017], Graph Sage [Hamilton et al., 2017], Gaussian Mixture Models (Mo Net) [Monti et al., 2017] and Gated GCN [Bresson and Laurent, 2017]. We leave out various linear architectures such as GIN [Xu et al., 2019] as they were shown to perform poorly on the benchmark. Learning tasks and datasets. As in Dwivedi et al. [2020] we consider (i) graph regression and the ZINC dataset [Irwin et al., 2012, Dwivedi et al., 2020]; (ii) vertex classification and the PATTERN and CLUSTER datasets [Dwivedi et al., 2020]; and (iii) link prediction and the COLLAB dataset [Hu et al., 2020]. |
| Dataset Splits | Yes | Graphs were divided between training/test as instructed by Dwivedi et al. [2020], and all numbers reported correspond to the test set. |
| Hardware Specification | Yes | All models for ZINC, PATTERN and COLLAB were trained on a Ge Force GTX 1080 Ti GPU, for CLUSTER a Tesla V100-SXM3-32GB GPU was used. |
| Software Dependencies | No | The paper mentions using DISC [Zhang et al., 2020] but does not provide specific version numbers for this or any other software libraries or dependencies used in the experiments. |
| Experiment Setup | Yes | Here we report results using 16 message-passing layers for ZINC, PATTERN, and CLUSTER, and 3 message-passing layers for COLLAB, as in Dwivedi et al. [2020]. In the supplementary material we report comparable results using only 4 layers for ZINC and PATTERN. We use the z-score of the logarithms of homomorphism counts to make them standard-normally distributed and comparable to other features. |