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..
On the Expressive Power of Spectral Invariant Graph Neural Networks
Authors: Bohang Zhang, Lingxiao Zhao, Haggai Maron
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically evaluate the expressive power of various GNN architectures studied in this paper. We adopt the BREC benchmark (Wang & Zhang, 2023), a comprehensive dataset for comparing the expressive power of GNNs. We focus on the following GNNs that are closely related to this paper: (i) Graphormer (Ying et al., 2021) (a distancebased GNN that uses SPD, see Section 5); (ii) NGNN (Zhang & Li, 2021) (a variant of subgraph GNN, see Section 4.1); (ii) ESAN (Bevilacqua et al., 2022) (an advanced subgraph GNN that adds cross-graph aggregations, see Section 4.1); (iv) PPGN (Maron et al., 2019a) (a higher-order GNN, see Section 6.3); (v) EPNN (this paper). We follow the same setup as in Wang & Zhang (2023) in both training and evaluation. For all baseline GNNs, the reported numbers are directly borrowed from Wang & Zhang (2023); For EPNN, we run the model 10 times with different seeds and report the average performance. Table 1. Empirical performance of different GNNs on BREC. |
| Researcher Affiliation | Collaboration | Bohang Zhang 1 Lingxiao Zhao 2 Haggai Maron 3 4 1Peking University 2Carnegie Mellon University 3Technion 4NVIDIA Research. Correspondence to: Bohang Zhang <EMAIL>, Haggai Maron <EMAIL>. |
| Pseudocode | No | The paper uses mathematical equations to describe algorithms and refinement rules (e.g., Equation 1, 2, 3), but does not present them in a labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Our code can be found in the following github repo: https://github.com/Lingxiao Shawn/EPNN-Experiments |
| Open Datasets | Yes | We adopt the BREC benchmark (Wang & Zhang, 2023), a comprehensive dataset for comparing the expressive power of GNNs. |
| Dataset Splits | No | We follow the same setup as in Wang & Zhang (2023) in both training and evaluation. For all baseline GNNs, the reported numbers are directly borrowed from Wang & Zhang (2023); For EPNN, we run the model 10 times with different seeds and report the average performance. |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU types, memory specifications) used for running the experiments are provided in the paper. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies (e.g., programming languages, libraries, or frameworks) used in the experiments. |
| Experiment Setup | No | The paper states, "We follow the same setup as in Wang & Zhang (2023) in both training and evaluation," but does not explicitly provide hyperparameter values or specific training configurations within the text. |