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..
Robust Influence Maximization for Hyperparametric Models
Authors: Dimitris Kalimeris, Gal Kaplun, Yaron Singer
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Additionally we validate our method empirically and prove that it outperforms the state-of-the-art robust influence maximization techniques. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Harvard University, Cambridge, MA, USA. Correspondence to: Dimitris Kalimeris <EMAIL>, Gal Kaplun <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 HIRO: Hyperparam Inf Robust Optimizer |
| Open Source Code | No | The paper does not provide any link or explicit statement about open-sourcing the code. |
| Open Datasets | No | The paper uses synthetically generated networks ('We generated four different synthetic networks...'), but does not provide any access information (link, DOI, citation) for a publicly available dataset. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits (e.g., percentages, sample counts, or references to predefined splits) needed for reproduction. |
| Hardware Specification | No | The paper does not mention any specific hardware (e.g., GPU/CPU models, memory, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x, or specific solvers). |
| Experiment Setup | Yes | We used the sigmoid function as the hyperparameteric model to determine the diffusion probabilities, i.e. h(θ xe) = 1 1+exp( θ xe) as in (Kalimeris et al., 2018). We generated d random features in [ 1, 1] for every edge. We used d = 5, however our results are consistent across a large range of dimensions d and featuregenerating techniques, such as normal or uniform distributions over the unit hyper-cube [ 1, 1]d and it s discrete analog { 1, 1}d. We sampled Θϵ = {θ1, . . . , θl} from Θ = [ B, B]d and generated the family of influence functions Fϵ = {fi | θi Θϵ} for l = 20. In addition we set T = 10 HIRO iterations with the exception of Experiments 1 and 2, where l and T are the free variable, respectively. |