On the Expressive Power of Spectral Invariant Graph Neural Networks
Authors: Bohang Zhang, Lingxiao Zhao, Haggai Maron
ICML 2024 | Conference PDF | Archive PDF | Plain Text | 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 <zhangbohang@pku.edu.cn>, Haggai Maron <hmaron@nvidia.com>. |
| 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. |