Parallel and Streaming Algorithms for K-Core Decomposition
Authors: Hossein Esfandiari, Silvio Lattanzi, Vahab Mirrokni
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we confirm the effectiveness of our sketching technique empirically on a number of publicly available graphs. |
| Researcher Affiliation | Industry | 1Google Research. Correspondence to: Hossein Esfandiari <esfandiari@googol.com>. |
| Pseudocode | Yes | Algorithm 1 Exclusive Coreness Labeling(H, Λ) |
| Open Source Code | No | The paper describes the implementation of the algorithms and experimental evaluation, but it does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We apply our sketch to eight real-world graphs available in the SNAP, Stanford Large Network Dataset Library (Leskovec & Sosiˇc, 2016): Enron (Klimt & Yang, 2004), Epinions (Richardson et al., 2003), Slashdot (Leskovec et al., 2009), Twitter (Mc Auley & Leskovec, 2012), Amazon (Yang & Leskovec, 2015), Youtube (Yang & Leskovec, 2015), Live Journal (Yang & Leskovec, 2015), Orkut (Yang & Leskovec, 2015) |
| Dataset Splits | No | The paper mentions using real-world graphs for experiments but does not specify any train/validation/test splits or reference standard splits from the datasets. |
| Hardware Specification | No | The paper discusses distributed and parallel algorithms, but it does not provide any specific details about the hardware (e.g., CPU/GPU models, memory, cluster specifications) used for running the experiments. |
| Software Dependencies | No | The paper describes algorithms and their implementation but does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | More specifically, we change line 14 to if lj(i) T pj = 1 where T is a parameter of our implementation. Furthermore, we also modify line 22 to pj+1 M pj, where M is modifiable multiplicative factor (that in Algorithm 2 is fixed to 2). We also slightly modify our Map Reduce algorithm to remove iteratively in parallel all nodes with degree smaller than 3 before sending the remaining graph to a single machine. |