Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
Authors: Shengchao Liu, Mehmet F. Demirel, Yingyu Liang
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 19,196 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,868 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL |
| 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. |