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..
Budgeted Online Influence Maximization
Authors: Pierre Perrault, Jennifer Healey, Zheng Wen, Michal Valko
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We propose an algorithm assuming an independent cascade diffusion model and edge-level semi-bandit feedback, and provide both theoretical and experimental results. |
| Researcher Affiliation | Collaboration | 1Adobe Research, San Jose, CA 2ENS Paris-Saclay 3Inria Lille 4Deep Mind. |
| Pseudocode | Yes | Algorithm 1 BOIM-CUCB ... Algorithm 2 GREEDY for ratio, Lazy implementation |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the methodology is available. |
| Open Datasets | Yes | We consider a subgraph of Facebook network (Leskovec and Krevl, 2014) |
| Dataset Splits | No | The paper does not explicitly provide details about training, validation, and test dataset splits. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software or libraries used in the experiments. |
| Experiment Setup | Yes | Input: ε > 0, B0 = B > 0. ... running over up to T = 10000 rounds. ... We take w U(0, 0.1) E and take deterministic, known costs with c 0 = 1, and c i = di/ maxj V dj. |