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..
Multi-Domain Generalized Graph Meta Learning
Authors: Mingkai Lin, Wenzhong Li, Ding Li, Yizhou Chen, Guohao Li, Sanglu Lu
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments based on four real-world graph domain datasets show that the proposed method significantly outperforms the state-of-the-art in multidomain graph meta learning tasks. |
| Researcher Affiliation | Academia | State Key Laboratory for Novel Software Technology, Nanjing University Nanjing, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Meta Training for MD-Gram |
| Open Source Code | No | The paper does not provide a direct link or explicit statement about the availability of its source code. |
| Open Datasets | Yes | The experiments are based on four real-world networks from different graph domains: (1) Product [P] (Hu et al. 2020): The Ogbn-products from Open Graph Benchmark... (2) Yelp [Y] (Zeng et al. 2019): A social network... (3) Reddit [R] (Hamilton, Ying, and Leskovec 2017): A graph dataset... (4) Academic [A] (Hu et al. 2020): An academic citation network named ogbn-papers100M from Open Graph Benchmark. |
| Dataset Splits | Yes | We consider the few-shot setting for a link prediction task that at most 30% edges is known beforehand, fixed 10% for validation and predict the rest edges following the setting of (Bose et al. 2019; Huang and Zitnik 2020). |
| Hardware Specification | Yes | The experiments are implemented with Pytorch in Python 3.6.8 and conducted on a PC with Intel Xeon E52620 v2 2.10GHz CPU, Ge Force RTX 2070 8G GPU and 64GB memory, running the 64-bit Cent OS Linux 7.2. |
| Software Dependencies | No | The paper mentions 'Pytorch' and 'Python 3.6.8' but does not specify the version for Pytorch, nor does it list other key libraries with their specific version numbers. |
| Experiment Setup | Yes | The unified node feature dimension is d = 256; learning rates are α1 = 0.001, α2 = α3 = 0.005; iteration numbers are r = 20, l = 10; hyperparameter for weighted loss is λ = 1 . |