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..
GATE: How to Keep Out Intrusive Neighbors
Authors: Nimrah Mustafa, Rebekka Burkholz
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experiments We validate the ability of GATE to perform the appropriate amount of neighborhood aggregation, as relevant for the given task and input graph, on both synthetic and real-world graphs. |
| Researcher Affiliation | Academia | 1CISPA Helmholtz Center for Information Security, 66123 Saarbr ucken, Germany. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our experimental code is available at https://github.com/Relational ML/GATE.git. |
| Open Datasets | Yes | On real-world datasets, GATE performs competitively on homophilic datasets and is substantially better than GAT on heterophilic datasets. Furthermore, up to our knowledge, it achieves a new state of the art on the relatively large OGB-arxiv dataset (i.e., 79.57 0.84% test accuracy). ... We evaluate GATE on relatively large-scale real-world node classification tasks, namely on five heterophilic benchmark datasets (Platonov et al., 2023) (see Table 3) and three OGB datasets (Hu et al., 2021) (see Table 5). |
| Dataset Splits | Yes | Nodes are divided randomly into train/validation/test split with a 2 : 1 : 1 ratio. ... Real-world datasets use their standard train/test/validation splits, i.e. those provided by Pytorch Geometric for Planetoid datasets Cora and Citeseer, by OGB framework for OGB datasets, and by (Platonov et al., 2023) for all remaining real-world datasets. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used to run its experiments. |
| Software Dependencies | No | The paper mentions Pytorch Geometric and Adam optimizer but does not specify their version numbers or the versions of other key software dependencies. |
| Experiment Setup | Yes | For synthetic datasets, the network width is fixed to 64 in all cases. ... For all synthetic data, a learning rate of 0.005 is used. Real-world datasets use their standard train/test/validation splits... the learning rate is adjusted for different real-world datasets to enable stable training of models as specified in Table 6. |