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..
Inferring Degrees from Incomplete Networks and Nonlinear Dynamics
Authors: Chunheng Jiang, Jianxi Gao, Malik Magdon-Ismail
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experimental Results We evaluated the performance of our approaches on six real networks, governed by three dynamical equations (ecological, regulatory, and epidemic) [Gao et al., 2016] (Table 1). |
| Researcher Affiliation | Academia | Chunheng Jiang , Jianxi Gao and Malik Magdon-Ismail Rensselaer Polytechnic Institute, Troy, NY, USA EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Zero Topo; Algorithm 2 Topo Plus; Algorithm 3 Round |
| Open Source Code | No | The paper does not contain any explicit statement about releasing open-source code for the described methodology or a direct link to a code repository. |
| Open Datasets | No | The paper mentions using 'six real networks' (Table 1) and refers to 'observed steady-states x of nodes', but it does not provide concrete access information (specific link, DOI, repository name, or formal citation with authors/year for dataset access) for these networks. |
| Dataset Splits | No | The paper describes uniform edge sampling fractions (p {10%, 20%, 30%}) for evaluation. However, it does not explicitly provide details about a distinct validation dataset split for hyperparameter tuning or model selection separate from the test/evaluation process. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | Algorithm 1 specifies 'Let t = 0 and x(0) eff= x' as an initial value. Algorithm 2 specifies 'Let t = 0 and ˆδ(t) = δ(s)'. Both algorithms include termination conditions like 'until ˆβ(t) and x(t) effdo not change', which are part of the experimental procedure. |