Balancing information exposure in social networks
Authors: Kiran Garimella, Aristides Gionis, Nikos Parotsidis, Nikolaj Tatti
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | 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. |