Graph Neural Networks Exponentially Lose Expressive Power for Node Classification
Authors: Kenta Oono, Taiji Suzuki
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally confirm that the proposed weight scaling enhances the predictive performance of GCNs in real data. |
| Researcher Affiliation | Collaboration | Kenta Oono1, 2, Taiji Suzuki1, 3 {kenta oono, taiji}@mist.i.u-tokyo.ac.jp 1The University of Tokyo 2Preferred Networks, Inc. 3RIKEN Center for Advanced Intelligence Project (AIP) |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/delta2323/gnn-asymptotics. |
| Open Datasets | Yes | We use Cora, Cite Seer, and Pub Med (Sen et al., 2008), which are standard citation network datasets. |
| Dataset Splits | Yes | We split all nodes in a graph (either Noisy Cora 2500/5000 or Noisy Cite Seer) into training, validation, and test sets. Data split is the same as the one done by Kipf & Welling (2017). |
| Hardware Specification | Yes | We conducted experiments in a signel machine which has 2 Intel(R) Xeon(R) Gold 6136 CPU@3.00GHz (24 cores), 192 GB memory (DDR4), and 3 GPGPUs (NVIDIA Tesla V100). |
| Software Dependencies | No | We used Chainer Chemistry, which is an extension library for the deep learning framework Chainer (Tokui et al., 2015; 2019), to implement GCNs and Optuna (Akiba et al., 2019) for hyperparameter tuning. The paper names software components but does not provide specific version numbers for them. |
| Experiment Setup | Yes | Table 3 shows the set of hyperparameters from which we chose. |