Pareto Optimization for Subset Selection with Dynamic Cost Constraints
Authors: Vahid Roostapour, Aneta Neumann, Frank Neumann, Tobias Friedrich2354-2361
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental investigations for the influence maximization in social networks show the advantage of POMC over generalized greedy algorithms. |
| Researcher Affiliation | Academia | Vahid Roostapour,1 Aneta Neumann,1 Frank Neumann,1 Tobias Friedrich1, 2 1Optimisation and Logistics, School of Computer Science, The University of Adelaide, Adelaide, Australia 2Chair of Algorithm Engineering, Hasso Plattner Institute, Potsdam, Germany |
| Pseudocode | Yes | Algorithm 1: Generalized Greedy Algorithm; Algorithm 2: Adaptive Generalized Greedy Algorithm; Algorithm 3: POMC Algorithm |
| Open Source Code | No | The paper states: 'We thank Chao Qian for providing his POMC implementation and test data to carry out our experimental investigations.' This indicates they used code provided by others, not that they are releasing their own code. No link or statement about code release is provided. |
| Open Datasets | Yes | To consider the cardinality constraint, we use the social news data which is collected from the social news aggregator Digg. ... (Hogg and Lerman 2012). |
| Dataset Splits | No | The paper does not provide specific details on dataset splits (e.g., percentages or counts for training, validation, or testing sets) required for reproducibility in the context of data partitioning for model training. |
| Hardware Specification | No | The paper does not specify any hardware details such as CPU/GPU models, memory, or specific computing environments used for the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or their version numbers (e.g., programming languages, libraries, or solvers with version numbers). |
| Experiment Setup | Yes | We assume that the initial constraint bound is B = 10 and stays within the interval [5, 30]. We consider a sequence of 200 constraint bounds obtained by randomly increasing or decreasing B by a value of 1. The values of B over time used in our studies are shown in Figure 2. For the experimental investigations of POMC, we consider a parameter τ which determines the number of generations between constraint changes. Furthermore, we consider the option of POMC having a warm-up phase where there are no dynamic changes for the first 10000 iterations. |