No Time to Observe: Adaptive Influence Maximization with Partial Feedback
Authors: Jing Yuan, Shaojie Tang
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | 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 jing.yuan@utallas.edu Shaojie Tang Naveen Jindal School of Management University of Texas at Dallas shaojie.tang@utdallas.edu |
| 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]. |