Bayesian Graph Neural Networks with Adaptive Connection Sampling
Authors: Arman Hasanzadeh, Ehsan Hajiramezanali, Shahin Boluki, Mingyuan Zhou, Nick Duffield, Krishna Narayanan, Xiaoning Qian
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results with ablation studies on benchmark datasets validate that adaptively learning the sampling rate given graph training data is the key to boosting the performance of GNNs in semi-supervised node classification, making them less prone to over-smoothing and over-fitting with more robust prediction. |
| Researcher Affiliation | Academia | 1Electrical and Computer Engineering Department, Texas A&M University, College Station, Texas, USA 2Mc Combs School of Business, The University of Texas at Austin, Austin, Texas, USA. |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/armanihm/GDC |
| Open Datasets | Yes | We consider Cora, Citeseer and Cora-ML datasets, and preprocess and split them same as Kipf & Welling (2017) and Bojchevski & Gunnemann (2018). |
| Dataset Splits | Yes | We train beta-Bernoulli GDC (BBGDC) models for 2000 epochs with a learning rate of 0.005 and a validation set used for early stopping. We consider Cora, Citeseer and Cora-ML datasets, and preprocess and split them same as Kipf & Welling (2017) and Bojchevski & Gunnemann (2018). |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., CPU, GPU models, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions implementing GCNs and using specific techniques but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | We train beta-Bernoulli GDC (BBGDC) models for 2000 epochs with a learning rate of 0.005 and a validation set used for early stopping. All of the hidden layers in our implemented GCNs have 128 dimensional output features. We use 5 × 10−3, 10−2, and 10−3 as L2 regularization factor for Cora, Citeseer, and Cora-ML, respectively. For the GCNs with more than 2 layers, we use warm-up during the first 50 training epochs to gradually impose the beta-Bernoulli KL term in the objective function. The temperature in the concrete distribution is set to 0.67. |