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..
Learning from Contagion (Without Timestamps)
Authors: Kareem Amin, Hoda Heidari, Michael Kearns
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide empirical evidence that our algorithm performs well more generally on realistic sparse graphs. 5.1. Experiments |
| Researcher Affiliation | Academia | Computer and Information Science, University of Pennsylvania |
| Pseudocode | Yes | Algorithm 1 Algorithm for exactly learning trees under passive seeds and independent cascade. Algorithm 2 K-lifts algorithm. |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about the availability of source code for the described methodology. |
| Open Datasets | Yes | On the real collaboration network, Net Science (Newman, 2003; Boccaletti et al., 2006; Newman, 2006) |
| Dataset Splits | No | The paper does not provide specific details on train/validation/test dataset splits. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models or cloud instance types) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned in the paper. |
| Experiment Setup | Yes | In all the experiments, each vertex becomes initially infected independently and with probability 0.05 (so roughly 5 vertices are seeded.) For simplicity, we will set the algorithm s parameter K = m(G), the correct number of edges, in each experiment. We run the experiments for M {1000, 10000, 100000, 1000000} and observe the relation between the performance of the K-lifts algorithm and the number of samples. |