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..
Interpretable Graph-Based Semi-Supervised Learning via Flows
Authors: Raif Rustamov, James Klosowski
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present an empirical evaluation both on synthetic and real data (Section 6). |
| Researcher Affiliation | Industry | Raif M. Rustamov, James T. Klosowski AT&T Labs Research |
| Pseudocode | No | The paper describes algorithms (e.g., ADMM in Section 5) but does not present them in a structured pseudocode block or algorithm figure. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Next we test the proposed method on high-dimensional image and text datasets used in (Zhou and Belkin 2011), including MNIST 3vs8, MNIST 4vs9, aut-avn, ccat, gcat, pcmac, and real-sim. |
| Dataset Splits | Yes | In each run we use nl = 50 labeled samples; in addition, we withheld 50 samples for validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as CPU/GPU models, memory, or cloud instance types. |
| Software Dependencies | No | The paper mentions 'ADMM' and 'Graphviz' but does not specify version numbers for any software dependencies, libraries, or solvers used in the experiments. |
| Experiment Setup | Yes | For p-Voltages, the value of p for each run is chosen from {1.0625, 1.125, 1.25, 1.5, 2} using the best performing value on the validation samples... For iterated Laplacian method, m is chosen from {1, 2, 4, 8, 16}... For the flow approach, the value of λ is chosen from {0, 0.025, 0.05, 0.1, 0.2}... |