Optimization of Chance-Constrained Submodular Functions
Authors: Benjamin Doerr, Carola Doerr, Aneta Neumann, Frank Neumann, Andrew Sutton1460-1467
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our theoretical work with an experimental investigation on the influence maximization problem in social networks. This investigation empirically analyzes the behavior of the greedy approaches for various stochastic settings. |
| Researcher Affiliation | Academia | 1Laboratoire d Informatique (LIX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France 2Sorbonne Universit e, CNRS, LIP6, Paris, France 3Optimisation and Logistics, School of Computer Science, The University of Adelaide, Adelaide, Australia 4Department of Computer Science, University of Minnesota Duluth, Duluth, MN, USA |
| Pseudocode | Yes | Algorithm 1: Greedy Algorithm (GA) and Algorithm 2: Generalized Greedy Algorithm (GGA) |
| Open Source Code | No | The paper does not provide any links to source code for the described methodology or make explicit statements about its public availability. |
| Open Datasets | Yes | We employ a graph obtained from social news data, with simple settings collected from a real-world data set obtained from the social news aggregator Digg. The Digg dataset (Hogg and Lerman 2012) contains stories submitted to the platform over a period of a month, and identification (IDs) of users who voted on the popular stories. |
| Dataset Splits | No | The paper mentions using the Digg dataset and its preprocessing, but it does not specify any training, validation, or test splits by percentages, sample counts, or references to predefined splits. It only states the total size: "We use the preprocessed data with 3523 nodes and 90244 edges". |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running the experiments. It only describes the experimental setup in terms of parameters and the dataset. |
| Software Dependencies | No | The paper does not specify any software names with version numbers for dependencies (e.g., programming languages, libraries, frameworks, or solvers). |
| Experiment Setup | Yes | For the experimental investigations of our GA, we consider all combinations of α = 0.1, 0.01, 0.001, 0.0001, and δ = 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0. We compare the results in terms of the fitness achieved at each α and δ level for budgets B = 20, 50, 100, 150. |