Active Learning in the Geometric Block Model
Authors: Eli Chien, Antonia Tulino, Jaime Llorca3641-3648
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the superior performance of our algorithms via numerical simulations on both real and synthetic datasets. |
| Researcher Affiliation | Collaboration | Eli Chien,1 Antonia Maria Tulino,2,3 Jaime Llorca2 1ECE, University of Illinois Urbana-Champaign, Illinois, 2Nokia Bell Labs, New Jersey, 3DIETI, University of Naples Federico II, Italy |
| Pseudocode | Yes | Algorithm 1: Motif-counting with S2 Algorithm 2: Aggressive edge removing approach |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | Political Blogs (PB): (Adamic and Glance 2005) Live Journal (LJ): (Yang and Leskovec 2015) |
| Dataset Splits | No | The paper does not provide explicit details about training/validation/test dataset splits with percentages, sample counts, or specific predefined split information. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models, processor types, or memory used for running experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies or libraries with their version numbers that were used in the experiments. |
| Experiment Setup | Yes | Experimental setting: For real-world networks, it is hard to obtain an exact threshold as the actual values of θ1 and θ2 are unknown. Hence, following the idea proposed in (Galhotra et al. 2018), we use a similar but much more intuitive approach compared with (Galhotra et al. 2018), which consists of 3 phases. [...] We choose T1 = 30 for the PB dataset and T1 = 5 for the LJ dataset. |