Learning on Large-scale Text-attributed Graphs via Variational Inference
Authors: Jianan Zhao, Meng Qu, Chaozhuo Li, Hao Yan, Qian Liu, Rui Li, Xing Xie, Jian Tang
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on multiple data sets demonstrate the efficiency and effectiveness of the proposed approach 1. |
| Researcher Affiliation | Collaboration | Jianan Zhao1,3 , Meng Qu1,3 , Chaozhuo Li2 , Hao Yan4, Qian Liu5, Rui Li6, Xing Xie2, Jian Tang1,7,8 1Mila Qu ebec AI Institute, 2Microsoft Research Asia, 3Universit e de Montr eal 4Central South University, 5Sea AI Lab, 6Dalian University of Technology 7HEC Montr eal, 8Canadian Institute for Advanced Research (CIFAR) |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1Codes are available at https://github.com/Andy JZhao/GLEM. |
| Open Datasets | Yes | Datasets. Three TAG node classification benchmarks are used in our experiment, including ogbnarxiv, ogbn-products, and ogbn-papers100M (Hu et al., 2020). |
| Dataset Splits | Yes | The statistics of these datasets are shown in Table 1. #Nodes #Edges Avg. Node Degree Train / Val / Test (%) ogbn-arxiv (Arxiv) 169,343 1,166,243 13.7 54 / 18 / 28 ogbn-products (Products) 2,449,029 61,859,140 50.5 8 / 2 / 90 ogbn-papers100M (Papers) 111,059,956 1,615,685,872 29.1 78 / 8 / 14 |
| Hardware Specification | Yes | The maximum batch size (max bsz.) and time/epoch are tested on a single NVIDIA Tesla V100 32GB GPU. |
| Software Dependencies | No | The paper mentions using DeBERTa as the LM model but does not provide specific version numbers for any software dependencies like programming languages, libraries (e.g., PyTorch, TensorFlow), or other packages. |
| Experiment Setup | Yes | For fair comparison against other feature learning methods such as GIANT, the hyper-parameters of GNNs are set to the best settings described in the paper or in the official repository, other parameters are tuned by grid search. |