Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Adaptive Budget Allocation for Maximizing Influence of Advertisements
Authors: Daisuke Hatano, Takuro Fukunaga, Ken-ichi Kawarabayashi
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 5 compares performance of the algorithms through computational experiments. We implemented three adaptive policies: Policies 1 and 2, and a sensitive greedy policy defined as follows. ... In addition to the adaptive policies, we implemented a nonadaptive greedy (1 1/e)-approximation algorithm [Soma et al., 2014]. We run the algorithms for instances of the bipartite influence model. |
| Researcher Affiliation | Academia | Daisuke Hatano, Takuro Fukunaga, Ken-ichi Kawarabayashi National Institute of Informatics, Japan JST, ERATO, Kawarabayashi Large Graph Project, Japan EMAIL |
| Pseudocode | Yes | Policy 1 Bicriteria (1 1/e)-Approximation Policy ... Policy 2 (e 1)/(2e)-Approximation Policy |
| Open Source Code | No | The paper does not contain any explicit statement that the authors' source code for the described methodology is publicly available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | For the experiments, we prepared a graph that represents user-user following information in Twitter [KONECT, 2014]. ... http://konect.uni-koblenz.de/networks/ego-twitter. |
| Dataset Splits | No | The paper states "We compute budget allocations over 500 instances by the policies, and compare their objective values by favg( ) for a policy ." and "the objective values are averaged over 500 instances for each k.", but it does not specify any training, validation, or test dataset splits or cross-validation setup for these instances. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper states "We implemented three adaptive policies" but does not provide specific version numbers for any software dependencies, libraries, or frameworks used in the implementation or experiments. |
| Experiment Setup | Yes | The parameters in the instances are set as follows: b(v) = 15 for all chosen nodes v, and the objective of the problem is defined as the maximization of the number of nodes influenced at least once. Budget k is set to a value in {20, 40, . . . , 200}. ... In the normal distribution, qvu(i) is given by exp( (i 15)2/50)/ 50 for each i 2 {1, . . . , 30} and vu 2 E; In the power law distribution, qvu(i) is given by exp(0.2(i 30))/10 for each i 2 {1, . . . , 30} and vu 2 E. |