Gradient Gating for Deep Multi-Rate Learning on Graphs
Authors: T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results are presented to demonstrate that the proposed framework achieves state-of-the-art performance on a variety of graph learning tasks, including on large-scale heterophilic graphs. |
| Researcher Affiliation | Collaboration | T. Konstantin Rusch ETH Z urich, ICSI and UC Berkeley Benjamin P. Chamberlain Charm Therapeutics Michael W. Mahoney ICSI, LBNL, and UC Berkeley Michael M. Bronstein University of Oxford Siddhartha Mishra ETH Z urich |
| Pseudocode | No | The paper provides mathematical formulations and a schematic diagram (Figure 1) of the architecture, but no formal pseudocode or algorithm blocks. |
| Open Source Code | No | No explicit statement or link indicating the public release of source code for the described methodology was found. |
| Open Datasets | Yes | We propose regression experiments based on the Wikipedia article networks Chameleon and Squirrel, (Rozemberczki et al., 2021). We test G2 on a node-level classification task with varying levels of homophily on the synthetic Cora dataset Zhu et al. (2020). In Table 2, we test the proposed framework on several real-world heterophilic graphs (with a homophily level of 0.30) (Pei et al., 2020; Rozemberczki et al., 2021) To this end, we consider three different experiments based on large graphs from Lim et al. (2021) |
| Dataset Splits | Yes | Table 1 shows the test normalized mean-square error (mean and standard deviation based on the ten pre-defined splits in Pei et al. (2020)) |
| Hardware Specification | Yes | All small and medium-scale experiments have been run on NVIDIA Ge Force RTX 2080 Ti, Ge Force RTX 3090, TITAN RTX and Quadro RTX 6000 GPUs. The large-scale experiments have been run on Nvidia Tesla A100 (40Gi B) GPUs. |
| Software Dependencies | No | The paper does not specify version numbers for any software dependencies or libraries used, only mentioning general tools common in machine learning. |
| Experiment Setup | Yes | All hyperparameters were tuned using random search. Table 7 shows the ranges of each hyperparameter as well as the random distribution used to randomly sample from it. |