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..
LD2: Scalable Heterophilous Graph Neural Network with Decoupled Embeddings
Authors: Ningyi Liao, Siqiang Luo, Xiang Li, Jieming Shi
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to showcase that our model is capable of lightweight minibatch training on large-scale heterophilous graphs, with up to 15 speed improvement and efficient memory utilization, while maintaining comparable or better performance than the baselines. |
| Researcher Affiliation | Academia | Ningyi Liao Nanyang Technological University EMAIL Siqiang Luo Nanyang Technological University EMAIL Xiang Li East China Normal University EMAIL Jieming Shi Hong Kong Polytechnic University EMAIL |
| Pseudocode | Yes | Algorithm 1 A2Prop: Approximate Adjacency Propagation |
| Open Source Code | Yes | Our code is available at: https://github.com/gdmnl/LD2. |
| Open Datasets | Yes | We mainly perform experiments on million-scale and above heterophilous datasets [26, 55] for the transductive node classification task, with the largest available graph wiki (m = 243M) included. |
| Dataset Splits | Yes | We leverage settings as per [26] such as the random train/test splits and the induced subgraph testing for GSAINT-sampling models. |
| Hardware Specification | Yes | Evaluations are conducted on a machine with 192GB RAM, two 28-core Intel Xeon CPUs (2.2GHz), and an NVIDIA RTX A5000 GPU (24GB memory). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with versions like Python 3.8 or PyTorch 1.9). |
| Experiment Setup | No | while parameter settings, further experiments, and subsequent discussions can be found in the Appendix. |