Improved Parallel Algorithms for Density-Based Network Clustering
Authors: Mohsen Ghaffari, Silvio Lattanzi, Slobodan Mitrović
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We design massively parallel computation (MPC) algorithms for these problems that are considerably faster than prior work. ... We complement our analysis with an experimental scalability analysis of our techniques. In this section we provide results of empirical evaluation of our k-core algorithms, while focusing on their scalability. |
| Researcher Affiliation | Collaboration | Mohsen Ghaffari * 1 Silvio Lattanzi * 2 Slobodan Mitrovi c * 3 *Equal contribution 1ETH Zurich 2Google Research Zurich 3MIT. Correspondence to: Mohsen Ghaffari <ghaffari@inf.ethz.ch>, Silvio Lattanzi <silviol@google.com>, Slobodan Mitrovi c <slobo@mit.edu>. |
| Pseudocode | Yes | Algorithm 1: A centralized algorithm for computing vertices with coreness above k; Algorithm 2: Computing a (1+2ε)-approximate coreness with respect to a threshold; Algorithm 3: Labeling vertices with coreness between k0.9 max and kmax; Algorithm 4: Labeling vertices by BELOW |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, such as a specific repository link, an explicit code release statement, or an indication of code in supplementary materials. |
| Open Datasets | Yes | We test the performances of our algorithms on public graphs of increasing size from the SNAP Large Networks Data Collection (Yang & Leskovec, 2015). |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. It discusses memory 'S' in the abstract and MPC model but not concrete hardware. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. It references general systems like MapReduce, Hadoop, Spark, and Dryad as abstractions but no specific implementation details. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings in the main text. It discusses comparison of algorithms and datasets but omits specific setup parameters. |