A Graph Dynamics Prior for Relational Inference
Authors: Liming Pan, Cheng Shi, Ivan Dokmanic
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that GDP reconstructs graphs far more accurately than earlier methods, with remarkable robustness to under-sampling. Experiments show that GDP achieves significantly higher inference accuracy than any of the earlier approaches. We consider several representative graph dynamical systems, both continuous and discrete, to validate the proposed algorithm. The results for NRI and GDP are averaged over 10 independent runs. Table 1 summarizes the average AUC with standard deviations. |
| Researcher Affiliation | Academia | 1School of Cyber Science and Technology, University of Science and Technology of China, Hefei, China 230026, 2School of Computer and Electronic Information, Nanjing Normal University, China 210023, 3 Departement Mathematik und Informatik, Universit at Basel, Basel, Switzerland 4051 |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. The model architecture is described textually and with a diagram. |
| Open Source Code | Yes | Reproducible code is available at https://github.com/Da Da Cheng/GDP. |
| Open Datasets | Yes | We consider several representative graph dynamical systems, both continuous and discrete, to validate the proposed algorithm. The continuous-time systems include (i) the Michaelis Menten kinetics (Karlebach and Shamir 2008)... (v) the Kuramoto model (Kuramoto 1975)... Moreover, we considered a publicly available f MRI dataset (viii) Netsim (Smith et al. 2011), comprising realistic simulated data. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) needed to reproduce the data partitioning for training, validation, and testing. It mentions data volume and averaging over runs, but not explicit splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | The hyperparameters for GDP are summarized in Appendix B.3. |