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 [1].
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. |