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..
Long-range Meta-path Search on Large-scale Heterogeneous Graphs
Authors: Chao Li, Zijie Guo, qiuting he, Kun He
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments across diverse heterogeneous datasets validate LMSPS s capability in discovering effective long-range meta-paths, surpassing state-of-the-art methods. |
| Researcher Affiliation | Collaboration | 1School of Computer Science and Technology, Huazhong University of Science and Technology 2 China Mobile Information Technology Co.,Ltd. 3 School of Computer Science, Fudan University |
| Pseudocode | Yes | Algorithm 1 The search algorithm of LMSPS |
| Open Source Code | Yes | Our code is available at https://github.com/JHL-HUST/LMSPS. |
| Open Datasets | Yes | We evaluate LMSPS on several representative heterogeneous graph datasets, including DBLP, IMDB, ACM, and Freebase from HGB benchmark [34], and the large-scale dataset OGBN-MAG from OGB challenge [20]. |
| Dataset Splits | Yes | target type nodes are divided into 24% for training, 6% for validation, and 70% for testing. ... For the OGBN-MAG dataset, we use the official data partition, where papers published before 2018, in 2018, and since 2019 are nodes for training, validation, and testing, respectively. |
| Hardware Specification | Yes | We use Pytorch [38] to run all experiments on one Tesla V100 GPU with 16GB GPU memory. |
| Software Dependencies | No | The paper mentions "Pytorch [38]" but does not specify a version number for Pytorch or any other software dependencies, such as specific Python libraries or CUDA versions. |
| Experiment Setup | Yes | We set the number of selected meta-paths M = 30 for all datasets. The final search space V = 60. The maximum hop is 6 for ogbn-mag, DBLP, 5 for IMDB, ACM, and 3 for Freebase. ... For searching in the super-net, we train for 200 epochs. ... τ linearly decays with the number of epochs from 8 to 4. The learning rate is 0.001 for all search stages and HGB training stage, and 0.003 for OGBN-MAG training stage. The weight decay is always 0. |