Hierarchical Transformer for Scalable Graph Learning
Authors: Wenhao Zhu, Tianyu Wen, Guojie Song, Xiaojun Ma, Liang Wang
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluations demonstrate that HSGT achieves stateof-the-art performance on large-scale benchmarks with graphs containing millions of nodes with high efficiency. |
| Researcher Affiliation | Collaboration | 1National Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University 2Yuanpei College, Peking University 3Microsoft 4Alibaba Group |
| Pseudocode | Yes | Algorithm 1 Overview of HSGT |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it explicitly state that the code is publicly available. |
| Open Datasets | Yes | We conduct experiments on nine benchmark datasets including four small-scale datasets (Cora, Cite Seer, Pub Med [Sen et al., 2008; Yang et al., 2016], Amazon-Photo [Shchur et al., 2018]) and six large-scale datasets (ogbnarxiv, ogbn-proteins, ogbn-products [Hu et al., 2020], Reddit [Hamilton et al., 2017], Flickr, Yelp [Zeng et al., 2019]). |
| Dataset Splits | Yes | We use the predefined dataset split if possible, or we set a random 1:1:8 train/valid/test split. |
| Hardware Specification | No | The paper mentions 'GPU memory usage' but does not specify any particular GPU models, CPU models, or other detailed hardware specifications used for experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch CUDA tools' but does not specify version numbers for any software dependencies required to replicate the experiment. |
| Experiment Setup | Yes | The number of hierarchical layers H and the coarsening ratios for each step α1, . . . , αH are predefined as hyperparameters. ... In Table 3, l stands for the number of layers for Graph SAGE and Graphormer-SAMPLE, while number of Transformer layers at horizontal blocks for HSGT. d stands for the hidden dimension for all models. ... Here we study the impact of p value with experiments and summarize the results in Table 5... |