Co-exposure Maximization in Online Social Networks
Authors: Sijing Tu, Cigdem Aslay, Aristides Gionis
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally demonstrate the quality of our proposal on real-world social networks.Finally, we experimentally evaluate our algorithm on several real-world datasets and demonstrate its superiority over several baselines.We evaluate our method against different baselines on real-world networks. |
| Researcher Affiliation | Academia | Sijing Tu Department of Computer Science KTH Royal Institute of Technology Stockholm, Sweden sijing@kth.se Cigdem Aslay Department of Computer Science Aarhus University Aarhus, Denmark cigdem@cs.au.dk Aristides Gionis Department of Computer Science KTH Royal Institute of Technology Stockholm, Sweden argioni@kth.se |
| Pseudocode | Yes | Algorithm 1: Pairs-Greedy |
| Open Source Code | Yes | Omitted proofs and implementation are provided as supplementary material. |
| Open Datasets | Yes | Datasets. We use the following networks: Flixster [6], Last.FM [5], Net HEPT [13], and Wiki Vote [30]. Basic statistics of these networks are reported in the supplementary. |
| Dataset Splits | No | The paper does not specify training, validation, and test dataset splits (e.g., percentages or exact counts) for reproducibility. It uses the term 'validation' in the context of a statistical test for estimating a lower bound, not for data partitioning. |
| Hardware Specification | Yes | Our experiments are performed on a server with a 2 × 10 core Xeon E5 2680 v2 2.80 GHz processor, with 256 GB memory. |
| Software Dependencies | No | The paper mentions various models and baselines but does not list specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | The confidence and accuracy parameters are set to ℓ = 1 and ϵ = 0.2. Our method takes as input seed budgets kr and kb... To ensure a fair comparison, we first execute Balance Exposure with varying k = 50, 100, 150, 200, and use the returned values kr and kb as input for the other methods. We fix τ = 2 and kr = 20. |