Mitigating Overexposure in Viral Marketing
Authors: Rediet Abebe, Lada Adamic, Jon Kleinberg
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | 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 red@cs.cornell.edu Lada A. Adamic University of Michigan ladamic@umich.edu Jon Kleinberg Cornell University kleinber@cs.cornell.edu |
| 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. |