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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Mitigating Overexposure in Viral Marketing
Authors: Rediet Abebe, Lada Adamic, Jon Kleinberg
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also present simulations of the model on real network topologies, quantifying the extent to which our optimal strategies outperform natural baselines. In this section, we present some computational results using datasets obtained from SNAP (Stanford Network Analysis Project). |
| Researcher Affiliation | Academia | Rediet Abebe Cornell University EMAIL Lada A. Adamic University of Michigan EMAIL Jon Kleinberg Cornell University EMAIL |
| Pseudocode | No | The paper describes the steps of its algorithm verbally but does not present them in a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the described methodology, nor does it explicitly state that the code is being released. |
| Open Datasets | Yes | In this section, we present some computational results using datasets obtained from SNAP (Stanford Network Analysis Project). In particular, we consider an email network from a European research institution (Paranjape, Benson, and Leskovec 2017; Leskovec, Kleinberg, and Faloutsos 2007) and a text message network from a social-networking platform at UC-Irvine (Panzarasa, Opsahl, and Carley 2009). |
| Dataset Splits | No | The paper describes running simulations with different parameter values and trials, but does not specify explicit training, validation, or test dataset splits for model development or evaluation. |
| Hardware Specification | No | The paper mentions simulation run times but does not provide any specific hardware details such as CPU or GPU models used for the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | Yes | The parameters τi θi σi for each agent are chosen as follows: we draw three numbers independently from an underlying distribution (we analyze both the uniform distribution on [0, 1] and the Gaussian distribution with mean 0.5 and standard deviation 0.1); we then sort these three numbers in non-decreasing order and set them to be τi, θi, and σi respectively. For each φ we run 100 trials and present the average payoff. |