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..
Supra-Laplacian Encoding for Transformer on Dynamic Graphs
Authors: Yannis Karmim, Marc Lafon, Raphael Fournier-S'niehotta, Nicolas THOME
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | SLATE outperforms numerous state-of-the-art methods based on Message-Passing Graph Neural Networks combined with recurrent models (e.g. , LSTM), and Dynamic Graph Transformers, on 9 datasets. Code is open-source and available at this link https://github.com/ykrmm/SLATE. We conduct an extensive experimental validation of our method across 11 real and synthetic discrete-time dynamic graph datasets. SLATE outperforms state-of-the-art results by a large margin. |
| Researcher Affiliation | Academia | Yannis Karmim Conservatoire National des Arts et Métiers CEDRIC, EA 4629 F 75003, Paris, France EMAIL Marc Lafon Conservatoire National des Arts et Métiers CEDRIC, EA 4629 F 75003, Paris, France EMAIL Raphaël Fournier S niehotta Conservatoire National des Arts et Métiers CEDRIC, EA 4629 F 75003, Paris, France EMAIL Nicolas Thome Sorbonne Université CNRS, ISIR F-75005 Paris, France EMAIL |
| Pseudocode | Yes | Algorithm 1: Computation of supra-laplacian spectrum |
| Open Source Code | Yes | Code is open-source and available at this link https://github.com/ykrmm/SLATE. |
| Open Datasets | Yes | Datasets. In Table 6 Appendix C, we provide detailed statistics for the datasets used in our experiments. An in-depth description of the datasets is given in Appendix C. We evaluate on DTDGs datasets provided by [60] and [55], we add a synthetic dataset SBM based on stochastic block model [29], to evaluate on denser DTDG. Table 6 lists: Can Parl, USLegis, Flights, Trade, UNVote, Contact, Hep Ph, AS733, Enron, Colab, SBM. |
| Dataset Splits | Yes | For the datasets from [60], we follow the same graph splitting strategy, which means 70% of the snapshots for training, 15% for validation, and 15% for testing. |
| Hardware Specification | Yes | We trained on an NVIDIA-Quadro RTX A6000 with 49 GB of total memory. |
| Software Dependencies | No | The paper mentions using a 'transformer Encoder Layer' [45], 'Flash Attention' [9], and 'Performer' [5], but does not provide specific version numbers for these or other software dependencies like Python or PyTorch. |
| Experiment Setup | Yes | Implementation details. We use one transformer Encoder Layer [45]. We fix the token dimension at d = 128 and the time window at w = 3 for all our experiments. We use an SGD optimizer for all of our experiments. Further details on hyper-parameters search, including the number of eigenvectors for our spatio-temporal encoding, are in Appendix D. Table 8: Hyperparameter search range. k [4,6,10,12,14], nhead_xa [1,2,4,8], nhead_encoder [1,2,4,8], dim_ffn [128,512,1024], norm_first [True,False], learning_rate [0.1,0.01,0.001,0.0001], weight_decay [0,5e-7]. |