Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks
Authors: Jintang Li, Zhouxin Yu, Zulun Zhu, Liang Chen, Qi Yu, Zibin Zheng, Sheng Tian, Ruofan Wu, Changhua Meng
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three large real-world temporal graph datasets demonstrate that Spike Net outperforms strong baselines on the temporal node classification task with lower computational costs. |
| Researcher Affiliation | Collaboration | Jintang Li1, Zhouxin Yu1, Zulun Zhu2, Liang Chen1*, Qi Yu2, Zibin Zheng1, Sheng Tian3, Ruofan Wu3, Changhua Meng3 1Sun Yat-sen University 2Rochester Institute of Technology 3Ant Group |
| Pseudocode | No | The paper includes equations and a high-level overview figure (Figure 2) of the Spike Net framework, but it does not present any formal pseudocode blocks or algorithms. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing the source code or provide a link to a code repository for the methodology described. |
| Open Datasets | Yes | In this section, we conduct experiments on three large real-world graph datasets: DBLP, Tmall (Lu et al. 2019), and Patent (Hall, Jaffe, and Trajtenberg 2001). The datasets statistics are listed in Table 1. |
| Dataset Splits | Yes | We follow (Lu et al. 2019) and examine the performance when different sizes of training datasets are used, i.e., 40%, 60%, and 80%, including 5% for validation. |
| Hardware Specification | Yes | The experiments were run on a Titan RTX GPU with the same batch size (except for Evolve GCN which is fully batch trained) for a fair comparison. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or specific library versions). |
| Experiment Setup | Yes | We cownduct temporal node classification on DBLP and Tmall by varying the values of τth and γ as {1.0, 0.9, 0.8, 0.7, 0.6} and {0., 0.1, 0.2, 0.3, 0.4}, respectively. We vary the smooth factor α from {0.5, 1.0, 2.0, 5.0, 10.0} to study the effects in the surrogate function σ( ). |