SALE-MLP: Structure Aware Latent Embeddings for GNN to Graph-free MLP Distillation
Authors: Harsh Pal, Sarthak Malik, Rajat Patel, Aakarsh Malhotra
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that SALE-MLP outperforms existing G2M methods across tasks and datasets, achieving 3 4% improvement in node classification for inductive settings while maintaining strong transductive performance. |
| Researcher Affiliation | Industry | Harsh Pal , Sarthak Malik , Rajat Patel , Aakarsh Malhotra AI Garage, Mastercard, Gurugram, Haryana, India EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Proposed SALE-MLP |
| Open Source Code | Yes | For more details on the implementation and additional results (including other unsupervised structural losses), read the supporting material1. 1https://github.com/ganzagun/SALE-MLP |
| Open Datasets | Yes | Datasets: The experimentation utilizes six widely-adopted public benchmark datasets: (Cora, Citeseer, Pubmed) [Sen et al., 2008], (Amazon-Photo, Amazon-Computer) [Feng et al., 2022], and the large-scale graph ogbn-arxiv [Hu et al., 2020]. |
| Dataset Splits | Yes | Furthermore, for Cora, Citeseer, and Pubmed, we follow splits from [Kipf and Welling, 2022] and for the Amazon dataset, we use splits from [Zhang et al., 2022a] i.e., using 20-shot for training, 30-shot for validation, and remaining for testing. While ogbn-arxiv uses standard splits [Hu et al., 2020]. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or processor types used for running its experiments. It only mentions inference time without specifying the hardware. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., Python, PyTorch, CUDA versions) needed to replicate the experiment. |
| Experiment Setup | Yes | For SALE-MLP, we perform a grid search for the hyper-parameters below on the validation data: # Hidden layers = {2,3} # Walks = {1,2,5} Walk len = {3,5,10} Pre-train Epochs = {1,2,5,10} λ = {0.0, 0.1, ... , 1.0} α = {1, 1.5, 2, 2.5, 3, 3.5, 4} Hidden layer Dimensionality = {64,128,256} |