Learning the Topology and Behavior of Discrete Dynamical Systems
Authors: Zirou Qiu, Abhijin Adiga, Madhav V. Marathe, S. S. Ravi, Daniel J. Rosenkrantz, Richard E. Stearns, Anil Vullikanti
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our results provide a theoretical foundation for learning both the topology and behavior of discrete dynamical systems. The work reported in this paper addresses some complexity and algorithmic issues associated with discrete dynamical systems that serve as formal models for contagion propagation in social networks. The results are in the form of theorems and algorithms for inferring the topology and interaction functions of dynamical systems. Our work does not involve experiments on public or private data. |
| Researcher Affiliation | Academia | 1 Computer Science Dept., University of Virginia. 2 Biocomplexity Institute, University of Virginia. 3 Computer Science Dept., University at Albany State University of New York. {zq5au, aa5ts, marathe, vsakumar}@virginia.edu, {ssravi0, drosenkrantz, thestearns2}@gmail.com |
| Pseudocode | Yes | Algorithm 1: Full-Infer-Matching (V) Input : The vertex set V; A training set O Output: A (MATCH,THRESH)-Sy DS S = (G, F) |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that source code for the described methodology is publicly available. The ethical statement notes that 'Our work does not involve experiments on public or private data,' which further suggests the absence of released code for empirical studies. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies with datasets. Although it refers to 'training examples' and 'training set' in the context of the PAC learning model, these are conceptual components of the theoretical framework and not references to specific, publicly available datasets used for experimentation. |
| Dataset Splits | No | This paper is a theoretical work and does not perform empirical experiments. Therefore, it does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and states that 'Our work does not involve experiments on public or private data.' As such, no hardware specifications for running experiments are provided. |
| Software Dependencies | No | The paper is a theoretical work and does not describe any software implementations or dependencies with specific version numbers. |
| Experiment Setup | No | The paper is a theoretical study focused on complexity and algorithmic issues, and it explicitly states that 'Our work does not involve experiments on public or private data.' Consequently, it does not detail any experimental setup or hyperparameters. |