Viral Clustering: A Robust Method to Extract Structures in Heterogeneous Datasets
Authors: Vahan Petrosyan, Alexandre Proutiere
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present extensive numerical experiments illustrating the robustness of the VC algorithm, and its superiority compared to existing algorithms. |
| Researcher Affiliation | Academia | Vahan Petrosyan and Alexandre Proutiere Royal Institute of Technology (KTH) {vahanp, alepro}@kth.se |
| Pseudocode | Yes | Algorithm 1: Spread Virus; Algorithm 2: Suppress Virus; Algorithm 3: Viral Clustering |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | C. We finally tested our algorithm on five real world datasets, collected from UCI machine learning repository (Lichman 2013). |
| Dataset Splits | No | The paper mentions using datasets and performing experiments but does not explicitly provide details on how the data was split into training, validation, and test sets. It mentions using '50 random realizations for each dataset' but not the split percentages or methodology. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper mentions using k-means and EM algorithm but does not specify any software names with version numbers. |
| Experiment Setup | Yes | For the VC algorithm, we have chosen lspr = 1 when p = 10 and lspr = 3 when p = 2, or 3. Only in Gap5, we used lspr = 20. ... For all real world and five artificially generated datasets we used lspr = 3. |