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..
Dynamic Spiking Graph Neural Networks
Authors: Nan Yin, Mengzhu Wang, Zhenghan Chen, Giulia De Masi, Huan Xiong, Bin Gu
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three large-scale real-world dynamic graph datasets validate the effectiveness of Dy-SIGN on dynamic node classification tasks with lower computational costs. |
| Researcher Affiliation | Collaboration | Nan Yin1, Mengzhu Wang2, Zhenghan Chen3, Giulia De Masi4, Huan Xiong5,1*, Bin Gu6,1* 1Mohamed bin Zayed University of Artificial Intelligence 2School of Artificial Intelligence, Hebei University of Technology 3Microsoft Corporation 4Technology Innovation Institute 5Harbin Institute of Technology 6Jilin University |
| Pseudocode | Yes | Algorithm 1: Learning Algorithm of Dy-SIGN |
| Open Source Code | No | The paper does not provide any concrete statement or link regarding the availability of its source code. |
| Open Datasets | Yes | We conduct experiments on three large real-world graph datasets, i.e., DBLP (Lu et al. 2019), Tmall (Lu et al. 2019) and Patent (Hall, Jaffe, and Trajtenberg 2001). |
| Dataset Splits | Yes | As for the implementation, we follow the same settings with (Li et al. 2023) and report the Macro-F1 and Micro-F1 results under different training ratios (i.e., 40%, 60%, and 80%). Besides, we use 5% for validation. |
| Hardware Specification | No | The paper mentions 'under the same experiment environment' regarding memory consumption but does not specify any particular hardware components like GPU or CPU models. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | The hidden dimension of all the methods is set to 128, and the batch size to 1024. The total training epochs are 100 and the learning rate is 0.001. |