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..
No Time to Observe: Adaptive Influence Maximization with Partial Feedback
Authors: Jing Yuan, Shaojie Tang
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 7 Experimental Evaluation We conduct extensive experiments on a real benchmark social networks: Net HEPT to examine the effectiveness and efficiency of the partial adaptive seeding algorithms. |
| Researcher Affiliation | Academia | Jing Yuan Department of Computer Science University of Texas at Dallas EMAIL Shaojie Tang Naveen Jindal School of Management University of Texas at Dallas EMAIL |
| Pseudocode | Yes | Algorithm 1 α-Greedy Policy: πu Algorithm 2 α-Greedy Policy with non-uniform cost: πnu Algorithm 3 Enhanced Greedy Policy πenhanced |
| Open Source Code | No | The paper does not provide any statement or link regarding the public availability of the source code for the methodology described. |
| Open Datasets | Yes | We conduct extensive experiments on a real benchmark social networks: Net HEPT to examine the effectiveness and efficiency of the partial adaptive seeding algorithms. We set the propagation probability of each directed edge randomly from i {0.01, 0.001} as in [Jung et al., 2012]. |
| Dataset Splits | No | The paper mentions using the 'Net HEPT dataset' for experiments but does not provide specific details on training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper's 'Experimental Evaluation' section describes the dataset and experimental parameters but does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as programming languages or library versions, needed to replicate the experiment. |
| Experiment Setup | Yes | We set the propagation probability of each directed edge randomly from i {0.01, 0.001} as in [Jung et al., 2012]. We adjust the value of control parameter α in range [0, 1]. ... the budget B ranges from 30 to 60. The cost of each node is randomly assigned from [1, 10]. |