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..
Balancing information exposure in social networks
Authors: Kiran Garimella, Aristides Gionis, Nikos Parotsidis, Nikolaj Tatti
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally evaluate our methods, on several real-world datasets. |
| Researcher Affiliation | Academia | Kiran Garimella Aalto University & HIIT Helsinki, Finland... Aristides Gionis Aalto University & HIIT Helsinki, Finland... Nikos Parotsidis University of Rome Tor Vergata Rome, Italy... Nikolaj Tatti Aalto University & HIIT Helsinki, Finland |
| Pseudocode | Yes | Algorithm 1: Common, greedy algorithm that only adds common seeds... Algorithm 2: Hedge, greedy algorithm, where each step is as good as adding the best common seed |
| Open Source Code | Yes | Moreover, our datasets and implementations are publicly available.2 https://users.ics.aalto.fi/kiran/BalanceExposure/ |
| Open Datasets | Yes | Our datasets and implementations are publicly available.2 https://users.ics.aalto.fi/kiran/BalanceExposure/ |
| Dataset Splits | No | The paper describes running experiments and simulations but does not specify train, validation, or test dataset splits or percentages for reproduction. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | We set the value of α to 0.8 for the heterogeneous setting. |