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..
Online Influence Maximization under Linear Threshold Model
Authors: Shuai Li, Fang Kong, Kejie Tang, Qizhi Li, Wei Chen
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This is the first theoretical result in such order for OIM under the LT model. |
| Researcher Affiliation | Collaboration | 1Shanghai Jiao Tong University 2Microsoft Research |
| Pseudocode | Yes | Algorithm 1 LT-Lin UCB; Algorithm 2 OIM-ETC |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not mention using any specific dataset for training or provide access information for one. |
| Dataset Splits | No | The paper is theoretical and does not provide specific dataset split information for validation. |
| Hardware Specification | No | The paper is theoretical and does not provide specific hardware details used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not contain specific experimental setup details. |