Notice: 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. Full accuracy metrics and methodology are described in [1].
Efficient Graph Similarity Computation with Alignment Regularization
Authors: Wei Zhuo, Guang Tan
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on real-world datasets demonstrate the effectiveness, efficiency and transferability of our approach. |
| Researcher Affiliation | Academia | Wei Zhuo Shenzhen Campus of Sun Yat-sen University EMAIL Guang Tan Shenzhen Campus of Sun Yat-sen University EMAIL |
| Pseudocode | No | The paper describes methods using text and equations but does not contain a structured pseudocode or algorithm block. |
| Open Source Code | Yes | 3.a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] See Supplemental Material |
| Open Datasets | Yes | We conduct experiments on four widely used GSC datasets including AIDS700, LINUX, IMDB [1], and NCI109 [2]. |
| Dataset Splits | Yes | Following the same splits as [1 3], i.e., 60%, 20%, and 20% of all graphs as training set, validation set, and query set, respectively. |
| Hardware Specification | Yes | all experiments are implemented with a single machine with 1 NVIDIA Quadro RTX 8000 GPU. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | T is a hyper-parameter controlling the output dimension, which is assigned as 16 for all datasets in our settings. For simplicity, we uniformly set p = 2 (i.e., ℓ2 distance) for all datasets, and analyze the sensitivity of the hyper-parameter p in Section 5.4. Combining AReg and GED discriminator, the training stage aims to minimize the following overall objective function L = LGED + λLAReg, where λ is an adjustable hyper-parameter controlling the strength of the regularization term. We give more implementation details of ERIC and baselines in Appendix B.2. |