N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
Authors: Shengchao Liu, Mehmet F. Demirel, Yingyu Liang
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on 60 tasks from 10 benchmark datasets demonstrate its advantages over both popular graph neural networks and traditional representation methods. |
| Researcher Affiliation | Academia | Shengchao Liu, Mehmet Furkan Demirel, Yingyu Liang Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI {shengchao, demirel, yliang}@cs.wisc.edu |
| Pseudocode | Yes | Algorithm 1 Vertex Embedding and Algorithm 2 Graph Embedding are provided on page 3. |
| Open Source Code | Yes | The code is available at https://github.com/chao1224/n_gram_graph. Baseline implementation follows [21, 44]. |
| Open Datasets | Yes | Regression datasets: Delaney [18], Malaria [23], CEP [29], QM7 [8], QM8 [43], QM9 [46]. Classification datasets: Tox21 [51], Clin Tox [24, 7], MUV [45], HIV [1]. |
| Dataset Splits | Yes | All datasets are split into five folds and with cross-validation results reported as follows. |
| Hardware Specification | No | The paper mentions 'CPU' and 'GPU' in Table 4 for timing measurements, and acknowledges 'computing resources from the University of Wisconsin-Madison Center for High Throughput Computing', but it does not specify any particular models (e.g., 'NVIDIA A100' or 'Intel Xeon'). |
| Software Dependencies | No | The paper mentions 'Py Torch Geometric' as part of the baseline implementation in footnote 3 (referencing [21]), but it does not provide specific version numbers for this or any other software dependencies. |
| Experiment Setup | Yes | We tune the hyperparameter carefully for all representation and modeling methods. More details about hyperparameters are provided in Section Appendix F. The following subsections display results with the N-gram parameter T = 6 and the embedding dimension r = 100. |