On Subset Selection with General Cost Constraints
Authors: Chao Qian, Jing-Cheng Shi, Yang Yu, Ke Tang
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on sensor placement and influence maximization with both cardinality and routing constraints exhibit the superior performance of POMC. |
| Researcher Affiliation | Academia | 1UBRI, School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China 2National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China |
| Pseudocode | Yes | Algorithm 1 Generalized Greedy Algorithm. Algorithm 2 POMC Algorithm. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of its own methodology's code. |
| Open Datasets | Yes | We use two real-world data sets: one (http://db.csail.mit.edu/labdata/labdata.html) is collected from sensors installed at 55 locations of the Intel Berkeley Research lab; the other [Zheng et al., 2013] is air quality data collected from 36 monitoring stations in Beijing. For cardinality constraints, we use a real-world data set (http://www.isi.edu/lerman/downloads/digg2009.html) collected from the social news website Digg. |
| Dataset Splits | No | The paper uses datasets for experiments but does not explicitly specify training, validation, or test splits with percentages, sample counts, or references to predefined splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | As POMC is a randomized algorithm, we repeat the run 10 times independently and report the average results. For cardinality and routing constraints, the budget B is set as {5, 6, . . . , 10} and {0.5, 0.6, . . . , 1}, respectively. For estimating the influence spread, i.e., the expected number of active nodes, we simulate the diffusion process 1,000 times independently and use the average as an estimation. For the number T of iterations of POMC, we used en BPmax/δˆc suggested by Theorem 2. |