Correlation Robust Influence Maximization
Authors: Louis Chen, Divya Padmanabhan, Chee Chin Lim, Karthik Natarajan
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An experimental study of using a distributionally robust model on real world datasets is provided in Section 5. |
| Researcher Affiliation | Academia | Louis Chen Naval Postgraduate School Monterey, California Divya Padmanabhan SUTD Singapore Chee Chin Lim SUTD Singapore Karthik Natarajan SUTD Singapore |
| Pseudocode | No | The paper contains mathematical formulations and algorithms described in text, but no distinct pseudocode or algorithm block is present. |
| Open Source Code | Yes | Code available at https://github.com/justanothergithubber/corr-im/ |
| Open Datasets | Yes | Our experiments were performed on two datasets (1) wikivote: Here each node denotes a user and each edge (i, j) denotes the action of user i voting for user j to be an admin [20]. ... (2) polblogs: Each node denotes a blog and each edge (i, j) denotes that blog i references blog j via a hyperlink [1]. |
| Dataset Splits | No | The paper mentions running Monte Carlo simulations and averaging results, but does not specify explicit training, validation, or test dataset splits for its experiments. |
| Hardware Specification | Yes | We used an ASUS laptop with i7-7500U processor for all experiments. |
| Software Dependencies | No | We used the igraph Python library [9] to represent the graphs and for the shortest path calculations. No version number specified for igraph. |
| Experiment Setup | No | The paper describes how edge probabilities are set (e.g., Unif(0, 1), Trivalency, Weighted cascade) and simulation methods, but it does not specify typical machine learning experimental setup details such as learning rates, batch sizes, or optimizer settings. |