Budgeted Online Influence Maximization
Authors: Pierre Perrault, Jennifer Healey, Zheng Wen, Michal Valko
ICML 2020 | Conference PDF | Archive PDF | Plain Text | 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. |