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..
Correlated Cascades: Compete or Cooperate
Authors: Ali Zarezade, Ali Khodadadi, Mehrdad Farajtabar, Hamid Rabiee, Hongyuan Zha
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on synthetic and two real datasets gathered from Twitter, URL shortening and music streaming services, illustrate the superior performance of the proposed model over the alternatives. |
| Researcher Affiliation | Academia | Sharif University of Technology, Azadi Ave, Tehran, Iran Georgia Institute of Tech., North Ave NW, Atlanta, GA 30332, United States |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Implementation codes and datasets can be found at https: //github.com/alikhodadadi/C4. |
| Open Datasets | Yes | We use the data crawled from Twitter (Hodas and Lerman 2014). |
| Dataset Splits | No | We set aside the last 20% of the data for the test set. The models are trained five times with 20% to 100% of the train data and β found by cross-validation. This describes test split and cross-validation for hyperparameter tuning, but not an explicit validation split percentage or count separate from the training data. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models or memory amounts) used for running the experiments are provided in the paper. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | The parameters of the models were drawn randomly from uniform distribution μi,p U(0, 0.1) and αi,j U(0, 0.01). Also, we set β = 1. In the correlated models, we set β = 0.1, 1, 100 to see the effect of mark function on the competitive or cooperative behavior of the proposed model. We trained 10 models, on 10% to 100% of the synthetic training data. |