Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Balancing information exposure in social networks
Authors: Kiran Garimella, Aristides Gionis, Nikos Parotsidis, Nikolaj Tatti
NeurIPS 2017 | Venue PDF | LLM Run Details | Input Tokens: 14,966 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,419 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |