Efficient Algorithms for Monotone Non-Submodular Maximization with Partition Matroid Constraint
Authors: Lan N. Nguyen, My T. Thai
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show our algorithms return comparative results to the state-of-the-art algorithms while taking much fewer queries. ... Experimental results show our algorithms return comparable solutions to the state-of-the-art techniques while totally outperform them in the number of queries. |
| Researcher Affiliation | Academia | Department of Computer and Information Science and Engineering University of Florida, Gainesville, Florida 32611 |
| Pseudocode | Yes | Algorithm 1 PROB; Algorithm 2 FASTPROB; Algorithm 3 GREEDY; Algorithm 4 THRGREEDY |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., links or explicit statements of code release) for the source code of the methodology described. |
| Open Datasets | Yes | With Boosting Influence Spread, we use Facebook dataset from SNAP database [Leskovec and Krevl, 2014], an undirected graph with 4,039 nodes and 88,234 edges. |
| Dataset Splits | No | The paper states 'The objective is estimated over 100 pre-sampled graph realizations of G.' and 'Results were averaged over 10 repetitions.' but does not specify training/validation/test dataset splits in terms of percentages, counts, or predefined splits for reproducing data partitioning in a machine learning context. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory amounts, or cloud computing instances) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software names with version numbers or other ancillary software details needed for replication. |
| Experiment Setup | Yes | With FASTPROB, we set δ = 0.001, which guarantees FASTPROB to return solutions almost similar to PROB but be much better in the number of queries. With THRGREEDY, we set ϵ = 0.5. |