Complete-Tree Space Favors Data-Efficient Link Prediction
Authors: Chi Gao, Lukai Li, Yancheng Zhou, Shangqi Guo
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments verify the data efficiency of CT space over other spaces. Moreover, leaf matching outperforms the state-of-the-art graph transformer in data-scarce scenarios while exhibiting excellent scalability. We experiment on six datasets, including the classic Cora, Pubmed, Citeseer (Yang et al., 2016; Li et al., 2024), the large-scale ogbl-collab, ogbl-ppa (Hu et al., 2020), and the temporal ICEWS18 (Liu et al., 2022). |
| Researcher Affiliation | Academia | 1Center for Brain-Inspired Computing Research, Department of Precision Instrument, Tsinghua University, Beijing, China 2Weixian College, Tsinghua University, Beijing, China. Correspondence to: Shangqi Guo <shangqi EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Greedy-Navigation-based Optimization |
| Open Source Code | Yes | The code is available at: https://github.com/Kevin Gao7/Leaf Matching. |
| Open Datasets | Yes | We experiment on six datasets, including the classic Cora, Pubmed, Citeseer (Yang et al., 2016; Li et al., 2024), the large-scale ogbl-collab, ogbl-ppa (Hu et al., 2020), and the temporal ICEWS18 (Liu et al., 2022). |
| Dataset Splits | Yes | For data splitting and the negative ratio in testing, we adhere to the settings established by Cog DL (Cen et al., 2023). |
| Hardware Specification | No | No specific hardware details (GPU/CPU models, memory, etc.) are provided in the paper. The paper only vaguely mentions 'excessive memory (on the order of terabytes)' in the context of an out-of-memory error for SEAL, but does not specify the hardware used for their own experiments. |
| Software Dependencies | No | The paper mentions using 'official implementation of node2vec, node2ket, and poincare' and 'official implementations of LPFormer, GAE, and SEAL provided in Hea RT (Li et al., 2023)', but it does not specify any software versions (e.g., Python, PyTorch, specific library versions). |
| Experiment Setup | Yes | For leaf matching, in Cora, Citeseer and Pubmed, we set h = 12, γ = 3, α = 8, c = 1, λ = 1, re = 0.8, training it with batch size 256 on 50 epochs. For ICEWS18, we set h = 14, γ = 3, α = 33, c = 3, λ = 1, re = 0.8, training it with batch size 32 on 50 epochs. For ogbl-collab, we set h = 16, γ = 3, α = 57, c = 3, λ = 1, re = 0.8, training it with batch size 256 on 50 epochs. |