Online Influence Maximization with Node-Level Feedback Using Standard Offline Oracles
Authors: Zhijie Zhang, Wei Chen, Xiaoming Sun, Jialin Zhang9153-9161
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We resolve the aforementioned challenges for the IC model and present the first e O(poly(|G|) T)-regret algorithm with node-level feedback using standard offline oracles. In the technical part, our main contribution is a novel adaptation of the maximum likelihood estimation (MLE) approach which can learn the edge-level parameters and their confidence ellipsoids based on the node-level feedback. Further, we adjust the update procedure to dissect the confidence ellipsoid into confidence intervals on each parameter, so that we can apply a standard offline influence maximization oracle instead of the pair-oracle. |
| Researcher Affiliation | Collaboration | Zhijie Zhang,1,2 Wei Chen,3 Xiaoming Sun,1,2 Jialin Zhang1,2, 1 Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China 2 University of Chinese Academy of Sciences, Beijing, China 3 Microsoft Research Asia, Beijing, China {zhangzhijie,sunxiaoming,zhangjialin}@ict.ac.cn, weic@microsoft.com |
| Pseudocode | Yes | Algorithm 1: IC-UCB and Algorithm 2: Estimate. |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on a specific dataset. Therefore, it does not mention or provide access to any public datasets. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with datasets, so it does not discuss training, validation, or test splits. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments requiring specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not report on experiments requiring specific software. Therefore, no software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper is theoretical, presenting algorithms and their regret analysis, rather than empirical results from experiments. Therefore, no experimental setup details like hyperparameters or training settings are provided. |