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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |